Pii - Detecting Personally Identifiable Information using Azure Cognitive Services

This article will look at detecting Person Identifiable Information (Pii) using Azure Cognitive Services. I have created a demo using .NET Maui Blazor has been created and the Github repo is here:
https://github.com/toreaurstadboss/PiiDetectionDemo

Person Identifiable Information (Pii) is desired to detect and also redact, that is using censorship or obscuring Pii to prepare documents for publication. The Pii feature in Azure Cognitive Services is a part of the Language resource service. A quickstart for using Pii is available here:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/personally-identifiable-information/quickstart?pivots=programming-language-csharp

After creating the Language resource, look up the keys and endpoints for you service. Using Azure CLI inside Cloud shell, you can enter this command to find the keys, in Azure many services has got two keys you can exchange with new keys through regeneration:

az cognitiveservices account keys list --resource-group SomeAzureResourceGroup --name SomeAccountAzureCognitiveServices
This is how you can query after endpoint of language resource using Azure CLI : az cognitiveservices account show --query "properties.endpoint" --resource-group SomeAzureResourceGroup --name SomeAccountAzureCognitiveServices
Next, the demo of this article. Connecting to the Pii Removal Text Analytics is possible using this Nuget package (REST calls can also be done manually): - Azure.AI.TextAnalytics version 5.3.0 Here is the other Nugets of my Demo included from the .csproj file :

PiiDetectionDemo.csproj

  <ItemGroup>
        <PackageReference Include="Azure.AI.TextAnalytics" Version="5.3.0" />
        <PackageReference Include="Microsoft.Maui.Controls" Version="$(MauiVersion)" />
        <PackageReference Include="Microsoft.Maui.Controls.Compatibility" Version="$(MauiVersion)" />
        <PackageReference Include="Microsoft.AspNetCore.Components.WebView.Maui" Version="$(MauiVersion)" />
        <PackageReference Include="Microsoft.Extensions.Logging.Debug" Version="8.0.0" />
    </ItemGroup>

A service using this Pii removal feature is simply making use of a TextAnalyticsClient and method RecognizePiiEntitiesAsync.

PiiRemovalTextClientService.cs IPiiRemovalTextClientService.cs

using Azure;
using Azure.AI.TextAnalytics;

namespace PiiDetectionDemo.Util
{
    public interface IPiiRemovalTextAnalyticsClientService
    {
        Task<Response<PiiEntityCollection>> RecognizePiiEntitiesAsync(string? document, string? language);
    }
}


namespace PiiDetectionDemo.Util
{
    public class PiiRemovalTextAnalyticsClientService : IPiiRemovalTextAnalyticsClientService
    {

        private TextAnalyticsClient _client;

        public PiiRemovalTextAnalyticsClientService()
        {
            var azureEndpoint = Environment.GetEnvironmentVariable("AZURE_COGNITIVE_SERVICE_ENDPOINT");
            var azureKey = Environment.GetEnvironmentVariable("AZURE_COGNITIVE_SERVICE_KEY");

            if (string.IsNullOrWhiteSpace(azureEndpoint))
            {
                throw new ArgumentNullException(nameof(azureEndpoint), "Missing system environment variable: AZURE_COGNITIVE_SERVICE_ENDPOINT");
            }
            if (string.IsNullOrWhiteSpace(azureKey))
            {
                throw new ArgumentNullException(nameof(azureKey), "Missing system environment variable: AZURE_COGNITIVE_SERVICE_KEY");
            }

            _client = new TextAnalyticsClient(new Uri(azureEndpoint), new AzureKeyCredential(azureKey));
        }

        public async Task<Response<PiiEntityCollection>> RecognizePiiEntitiesAsync(string? document, string? language)
        {
            var piiEntities = await _client.RecognizePiiEntitiesAsync(document, language);
            return piiEntities;
        }

    }
}

The UI codebehind of the razor component page showing the UI looks like this:

Home.razor.cs

using Azure;
using Microsoft.AspNetCore.Components;
using PiiDetectionDemo.Models;
using PiiDetectionDemo.Util;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;

namespace PiiDetectionDemo.Components.Pages
{
    public partial class Home
    {

        private IndexModel Model = new();
        private bool isProcessing = false;
        private bool isSearchPerformed = false;

        private async Task Submit()
        {
            isSearchPerformed = false;
            isProcessing = true;
            try
            {
                var response = await _piiRemovalTextAnalyticsClientService.RecognizePiiEntitiesAsync(Model.InputText, null);
                Model.RedactedText = response?.Value?.RedactedText;
                Model.UpdateHtmlRedactedText();
                Model.AnalysisResult = response?.Value;
                StateHasChanged();
            }
            catch (Exception ex)
            {
                await Console.Out.WriteLineAsync(ex.ToString());
            }
            isProcessing = false;
            isSearchPerformed = true;
        }

        private void removeWhitespace(ChangeEventArgs args)
        {
            Model.InputText = args.Value?.ToString()?.CleanupAllWhiteSpace();
            StateHasChanged();
        }



    }
}

To get the redacted or censored text void of any Pii that the Pii detection feature was able to detect, access the Value of type Azure.AI.TextAnalytics.PiiEntityCollection. Inside this object, the string RedactedText contains the censored / redacted text. The IndexModel looks like this :

using Azure.AI.TextAnalytics;
using Microsoft.AspNetCore.Components;
using PiiDetectionDemo.Util;
using System.ComponentModel.DataAnnotations;
using System.Text;

namespace PiiDetectionDemo.Models
{

    public class IndexModel
    {

        [Required]
        public string? InputText { get; set; }

        public string? RedactedText { get; set; }

        public string? HtmlRedactedText { get; set; }

        public MarkupString HtmlRedactedTextMarkupString { get; set; }

        public void UpdateHtmlRedactedText()
        {
            var sb = new StringBuilder(RedactedText);
            if (AnalysisResult != null && RedactedText != null)
            {
                foreach (var piiEntity in AnalysisResult.OrderByDescending(a => a.Offset))
                {
                    sb.Insert(piiEntity.Offset + piiEntity.Length, "</b></span>");
                    sb.Insert(piiEntity.Offset, $"<span style='background-color:lightgray;border:1px solid black;corner-radius:2px; color:{GetBackgroundColor(piiEntity)}' title='{piiEntity.Category}: {piiEntity.SubCategory} Confidence: {piiEntity.ConfidenceScore} Redacted Text: {piiEntity.Text}'><b>");
                }
            }
            HtmlRedactedText = sb.ToString()?.CleanupAllWhiteSpace();    
            HtmlRedactedTextMarkupString = new MarkupString(HtmlRedactedText ?? string.Empty);
        }

        private string GetBackgroundColor(PiiEntity piiEntity)
        {
            if (piiEntity.Category == PiiEntityCategory.PhoneNumber)
            {
                return "yellow";
            }
            if (piiEntity.Category == PiiEntityCategory.Organization)
            {
                return "orange";
            }
            if (piiEntity.Category == PiiEntityCategory.Address)
            {
                return "green";
            }
            return "gray";                   
        }

        public long ExecutionTime { get; set; }
        public PiiEntityCollection? AnalysisResult { get; set; }

    }
}

Frontend UI looks like this: Home.razor

@page "/"
@using PiiDetectionDemo.Util

@inject IPiiRemovalTextAnalyticsClientService _piiRemovalTextAnalyticsClientService;

<h3>Azure HealthCare Text Analysis - Pii detection feature - Azure Cognitive Services</h3>

<em>Pii = Person identifiable information</em>

<EditForm Model="@Model" OnValidSubmit="@Submit">
    <DataAnnotationsValidator />
    <ValidationSummary />

    <div class="form-group row">
        <label><strong>Text input</strong></label>
        <InputTextArea @oninput="removeWhitespace" class="overflow-scroll" style="max-height:500px;max-width:900px;font-size: 10pt;font-family:Verdana, Geneva, Tahoma, sans-serif" @bind-Value="@Model.InputText" rows="5" />
    </div>

    <div class="form-group row">
        <div class="col">
            <br />
            <button class="btn btn-outline-primary" type="submit">Run</button>
        </div>
        <div class="col">
        </div>
        <div class="col">
        </div>
    </div>

    <br />

    @if (isProcessing)
    {

        <div class="progress" style="max-width: 90%">
            <div class="progress-bar progress-bar-striped progress-bar-animated"
                 style="width: 100%; background-color: green">
                Retrieving result from Azure Text Analysis Pii detection feature. Processing..
            </div>
        </div>
        <br />

    }

    <div class="form-group row">
        <label><strong>Analysis result</strong></label>

        @if (isSearchPerformed)
        {
            <br />
            <b>Execution time took: @Model.ExecutionTime ms (milliseconds)</b>

            <br />
            <br />

            <b>Redacted text (Pii removed)</b>
            <br />

            <div class="form-group row">
               <label><strong>Categorized Pii redacted text</strong></label>
               <div>
               @Model.HtmlRedactedTextMarkupString
               </div>
            </div>

            <br />
            <br />

            <table class="table table-striped table-dark table-hover">
                <thead>
                <th>Pii text</th>
                <th>Category</th>
                <th>SubCategory</th>
                <th>Offset</th>
                <th>Length</th>
                <th>ConfidenceScore</th>
                </thead>
                <tbody>
                    @if (Model.AnalysisResult != null) {
                        @foreach (var entity in Model.AnalysisResult)
                        {
                            <tr>
                                <td>@entity.Text</td>
                                <td>@entity.Category.ToString()</td>
                                <td>@entity.SubCategory</td>
                                <td>@entity.Offset</td>
                                <td>@entity.Length</td>
                                <td>@entity.ConfidenceScore</td>                                        
                            </tr>
                        }
                    }
                </tbody>
            </table>

        }
    </div>

</EditForm>

The Demo uses Bootstrap 5 to build up a HTML table styled and showing the Azure.AI.TextAnalytics.PiiEntity properties.

Building a filter via Expression trees in C#

This article will look at how to build a filter with Expression trees in C#.

It is an academic exercise how to use Expression trees, you would probably use filters just specifying lambda function with LINQ, but the code shows how you can build an Expression incrementally and compile it to a function. If there is a use-case where LINQ does not fit, perhaps some late-binding scenario or where LINQ does not offer an operator, you can use the approach shows in this article, but the article shows simple usage of Expression trees for introducing Expression trees to C# developers wanting to
test them out in more detail.

The sample code below shows the sample code testing out how to build the Expression incrementally using extension method loading some sample data. An important gotcha is to keep sending in same the objectParameter which is the parameter expression used in the lambda function that is built up, this must be the same parameter. Consider some lambda function of an object 'Person' where the parameter 'x' like:

x => x.Age > 3 && x.Age < 9

The point is that the ParameterExpression x must be the same object, or else we get an error.

FilterHelper.cs

public static class FilterHelper {

	public enum ComparisonOperator {
		Equal,
		LessThan,
		LessThanOrEqual,
		GreaterThan,
		GreaterThanOrEqual,
		NotEqual		
	}
	
	public static Func<TClass, bool> CompileFilter<TClass>(this Expression expression, ParameterExpression objectParameter){
		var expr = Expression.Lambda<Func<TClass, bool>>(expression, false, new List<ParameterExpression>{ objectParameter });
		return expr.Compile();
	}
	
	public static Expression BuildFilter<TClass, TProp>(this Expression previousExpression, Expression<Func<TClass, TProp>> prop, object value,
		ComparisonOperator op, ParameterExpression objectParameter){
		
		var propertyInfo = GetPropertyInfo(prop);
		var propertyToCall = Expression.Property(objectParameter, propertyInfo);		
		var valueToTest = Expression.Constant(value);
		
		Expression operatorExpression = null;
		switch (op)
		{
			case ComparisonOperator.Equal:
				operatorExpression = Expression.Equal(propertyToCall, valueToTest);
				break;
			case ComparisonOperator.NotEqual:
				operatorExpression = Expression.NotEqual(propertyToCall, valueToTest);
				break;
			case ComparisonOperator.LessThan:
				operatorExpression = Expression.LessThan(propertyToCall, valueToTest);
				break;
			case ComparisonOperator.LessThanOrEqual:
				operatorExpression = Expression.LessThanOrEqual(propertyToCall, valueToTest);
				break;
			case ComparisonOperator.GreaterThan:
				operatorExpression = Expression.GreaterThan(propertyToCall, valueToTest);
				break;
			case ComparisonOperator.GreaterThanOrEqual:
				operatorExpression = Expression.GreaterThanOrEqual(propertyToCall, valueToTest);
				break;
		}
		
		if (previousExpression == null){
			return operatorExpression;
		}
		else {
			return Expression.AndAlso(previousExpression, operatorExpression);
		}			
	}

	/// <summary>
	/// Gets the corresponding <see cref="PropertyInfo" /> from an <see cref="Expression" />.
	/// </summary>
	/// <param name="property">The expression that selects the property to get info on.</param>
	/// <returns>The property info collected from the expression.</returns>
	/// <exception cref="ArgumentNullException">When <paramref name="property" /> is <c>null</c>.</exception>
	/// <exception cref="ArgumentException">The expression doesn't indicate a valid property."</exception>
	private static PropertyInfo GetPropertyInfo<T, P>(Expression<Func<T, P>> property)
	{
		if (property == null)
		{
			throw new ArgumentNullException(nameof(property));
		}

		if (property.Body is UnaryExpression unaryExp)
		{
			if (unaryExp.Operand is MemberExpression memberExp)
			{
				return (PropertyInfo)memberExp.Member;
			}
		}
		else if (property.Body is MemberExpression memberExp)
		{
			return (PropertyInfo)memberExp.Member;
		}

		throw new ArgumentException($"The expression doesn't indicate a valid property. [ {property} ]");
	}

}

The sample data uses a POCO Employee as entity class: Employee.cs

public class Employee
{
	public int Id { get; set; }
	public string FirstName { get; set; }
	public string LastName { get; set; }
	public string Department { get; set; }
	public string Position { get; set; }
	public decimal Salary { get; set; }
	public DateTime HireDate { get; set; }
	public DateOnly HireDateOnly
	{
		get { return DateOnly.FromDateTime(HireDate); }
	}
}

The sample program loads up the Json data, then it builds the expression with method BuildFilter shown above and then finally calls CompileFilter to build the expression into a Func<TClass, bool> where TClass is the employee type.

Program.cs

void Main()
{
	string json = File.ReadAllText(Path.Combine(@"C:\Users\SomeUser\Documents\LINQPad Queries\SampleData\Employees.json"));
	var employees = JsonSerializer.Deserialize<List<Employee>>(json, 
new JsonSerializerOptions { PropertyNameCaseInsensitive = true });
	//employees.Dump();

	var objectParameter = Expression.Parameter(typeof(Employee));
	Expression currentFilter = null;
	currentFilter = FilterHelper.BuildFilter<Employee, object>(currentFilter, e => e.Department,
 "Engineering", FilterHelper.ComparisonOperator.Equal, objectParameter)
		.BuildFilter<Employee, object>(e => e.Salary, 79000m, 
FilterHelper.ComparisonOperator.GreaterThan, objectParameter);

	Func<Employee, bool> employeeFilter = currentFilter.CompileFilter<Employee>(objectParameter);
	var matchingEmployees = employees.Where(employeeFilter).ToList();
	matchingEmployees.Dump();
}

Sample data json looks like this - an array of employees

Employees.json

[
    {
      "id": 1,
      "firstName": "Alice",
      "lastName": "Johnson",
      "department": "HR",
      "position": "Manager",
      "salary": 60000,
      "hireDate": "2022-03-15"
    },
    {
      "id": 2,
      "firstName": "Bob",
      "lastName": "Smith",
      "department": "Engineering",
      "position": "Software Engineer",
      "salary": 80000,
      "hireDate": "2021-09-10"
    },
    {
      "id": 3,
      "firstName": "Charlie",
      "lastName": "Brown",
      "department": "Finance",
      "position": "Financial Analyst",
      "salary": 70000,
      "hireDate": "2020-05-20"
    },
    {
      "id": 4,
      "firstName": "David",
      "lastName": "Lee",
      "department": "Marketing",
      "position": "Marketing Specialist",
      "salary": 65000,
      "hireDate": "2019-11-05"
    },
    {
      "id": 5,
      "firstName": "Eva",
      "lastName": "Garcia",
      "department": "Sales",
      "position": "Sales Representative",
      "salary": 75000,
      "hireDate": "2018-07-12"
    },
    {
      "id": 6,
      "firstName": "Frank",
      "lastName": "Wang",
      "department": "Engineering",
      "position": "Senior Developer",
      "salary": 95000,
      "hireDate": "2017-02-28"
    },
    {
      "id": 7,
      "firstName": "Grace",
      "lastName": "Miller",
      "department": "HR",
      "position": "Recruiter",
      "salary": 55000,
      "hireDate": "2016-08-18"
    },
    {
      "id": 8,
      "firstName": "Henry",
      "lastName": "Chen",
      "department": "Finance",
      "position": "Financial Manager",
      "salary": 90000,
      "hireDate": "2015-04-03"
    },
    {
      "id": 9,
      "firstName": "Ivy",
      "lastName": "Nguyen",
      "department": "Marketing",
      "position": "Content Writer",
      "salary": 60000,
      "hireDate": "2014-10-22"
    },
    {
      "id": 10,
      "firstName": "Jack",
      "lastName": "Kim",
      "department": "Sales",
      "position": "Account Executive",
      "salary": 80000,
      "hireDate": "2013-06-14"
    },
    {
      "id": 11,
      "firstName": "Karen",
      "lastName": "Taylor",
      "department": "Engineering",
      "position": "QA Engineer",
      "salary": 75000,
      "hireDate": "2012-01-09"
    },
    {
      "id": 12,
      "firstName": "Leo",
      "lastName": "Rodriguez",
      "department": "HR",
      "position": "HR Specialist",
      "salary": 55000,
      "hireDate": "2011-07-27"
    },
    {
      "id": 13,
      "firstName": "Mia",
      "lastName": "Liu",
      "department": "Finance",
      "position": "Financial Advisor",
      "salary": 70000,
      "hireDate": "2010-03-16"
    },
    {
      "id": 14,
      "firstName": "Nina",
      "lastName": "Martinez",
      "department": "Marketing",
      "position": "Social Media Manager",
      "salary": 65000,
      "hireDate": "2009-09-05"
    },
    {
      "id": 15,
      "firstName": "Oscar",
      "lastName": "Hernandez",
      "department": "Sales",
      "position": "Sales Manager",
      "salary": 100000,
      "hireDate": "2008-04-21"
    }
  ]

The filter is more limited than just sticking to LINQ, but the code in this example shows how you can build a filter incrementally. Traditionally, you would use Linq and an IEnumerable of TClass and you can keep on filter it too. Here are some closing arguments for why you could make use of Expression trees and have to use them too and not be able to use Linq:

Purpose: Expression trees represent code as data structures. They allow you to build executable code dynamically in C#.
Use Cases:
- Dynamic Code Generation: When you need to create or modify code at runtime (e.g., building custom queries or transformations).
- Remote Execution: Expression trees are useful for scenarios where you want to send calculations across the wire (e.g., database queries, web services).
- Custom Query Providers: If you’re building your own query provider (like LINQ to SQL or Entity Framework), expression trees help translate queries into other formats (e.g., SQL).

Functional programming - Fork combinator in C# to combine results from parts

This article will discuss a wellknown combinator called Fork which allows you to combine the mapped result. Consider the following extension methods to fork on an object. Fork here means to operate on parts of the object such as
different properties and apply functions on these parts and then recombine the results into a combined result via a specified combinator function, sometimes called a 'join function'.

public static class FunctionalExtensions {

	public static TOutput Map<TInput, TOutput>(
		this TInput @this,
		Func<TInput, TOutput> func) => func(@this);

	public static TOutput Fork<TInput, TMiddle, TOutput>(
		this TInput @this,
		Func<IEnumerable<TMiddle>, TOutput> combineFunc,
		params Func<TInput, TMiddle>[] parts)
	{
		var intermediateResults = parts.Select(p => p(@this));
		var result = combineFunc(intermediateResults);
		return result;
    }

	public static TOutput Fork<TInput, TMiddle, TOutput>(
		this TInput @this,
		Func<TInput, TMiddle> leftFunc,
		Func<TInput, TMiddle> rightFunc,
		Func<TMiddle, TMiddle, TOutput> combineFunc)
	{
		var leftResult = leftFunc(@this); // @this.Map(leftFunc);
		var rightResult = rightFunc(@this); // @this.Map(rightFunc);
		var combineResult = combineFunc(leftResult, rightResult);
		return combineResult;
	}

}

Let's take a familiar mathematical example, calculating the Hypotenuse in a triangle using Pythagorean theorem. This states that the length of the longest side A of a 'right triangle' is the square root of the sum of the squares of the shorter sides B and C : A = √(B² + C²) Consider this class:

  
  
  public class Triangle {
	public double CathetusA { get; set; }
	public double CathetusB { get; set; }	
	public double Hypotenuse { get; set; }
  }
  
    

Let's test the first Fork helper extension method accepting two functions for specifying the left and right components:

  
  
  	var triangle = new Triangle
	{
		CathetusA = 3,
		CathetusB = 4
	};
	
	triangle.Hypotenuse = triangle.Fork(	
		t => t.CathetusA * t.CathetusA, 
		t => t.CathetusB * t.CathetusB, 
		(l,r) => Math.Sqrt(l+r));
		
	Console.WriteLine(triangle.Hypotenuse);
  
  
  

This yields '5' as the answer via the forked result above. A simple example, but this allows us to create a simple combinatory logic example on an object of any type using functional programming (FP). Let's look at a simpler example just combining multiple properties of an object with a simple string-join, but using the Fork version supporting arbitrary number of parts / components:

 


public class Person {
	public string JobTitle { get; set; }
	public string FirstName { get; set; }
	public IEnumerable<string> MiddleNames { get; set; }
	public string LastName { get; set; }
}

var person = new Person{
		JobTitle = "Detective",
		FirstName = "Alexander",
		MiddleNames = new[] { "James", "Axel" },
		LastName = "Foley"
	};
	
string contactCardText = person.Fork(parts => string.Join(" ", parts), p => p.FirstName,
 p => string.Join(" ", p.MiddleNames), p => p.LastName);
Console.WriteLine(contactCardText);

This yields: Alexander James Axel Foley Fork can be very useful in many cases you need to 'branch off' on an object and recombine parts of the object with some specific function, either two parts or multiple parts and either continue to work on the results or retrieve the results.

Functional programming - the Tee function to inspect current state in a chained expression

In this article we will look at helper extension methods of StringBuilder first to better support chaining StringBuilder. We will work on the same StringBuilder instance and add support for appending lines or character to the StringBuilder given a condition. Also example showing how to aggregate lines from a sequence is shown and appending formatted lines. Since C# interpolation has become more easy to use, I would suggest you keep using AppendLine instead. Here is the helper methods in the extension class :

public static class StringBuilderExtensions {

	public static StringBuilder AppendSequence<T>(this StringBuilder @this, IEnumerable<T> sequence, Func<StringBuilder, T, StringBuilder> fn)
	{
		var sb = sequence.Aggregate(@this, fn);
		return sb;
	}
	
	public static StringBuilder AppendWhen(this StringBuilder @this, Func<bool> condition, Func<StringBuilder, StringBuilder> fn) => 
		condition() ? fn(@this) : @this;
		
    public static StringBuilder AppendFormattedLine(
		this StringBuilder @this,
		string format,
		params object[] args) => 
			@this.AppendFormat(format, args).AppendLine();
	
}

Now consider this example usage:

void Main()
{
	var countries = new Dictionary<int, string>{
		{ 1, "Norway" },
		{ 2, "France" },
		{ 3, "Austria" },
		{ 4, "Sweden" },
		{ 5, "Finland" },
		{ 6, "Netherlands" }
	};
	string options = BuildSelectBox(countries, "countriesSelect", true);
	options.Dump("Countries"); //dump is a method available in Linqpad to output objects 
	
}

private static string BuildSelectBox(IDictionary<int, string> options, string id, bool includeUnknown) =>
		new StringBuilder()
			.AppendFormattedLine($"<select id=\"{id}\" name=\"{id}\">")
			.AppendWhen(() => includeUnknown, sb => sb.AppendLine("\t<option value=\"0\">Unknown</option>"))
			.AppendSequence(options, (sb, item) => sb.AppendFormattedLine("\t<option value=\"{0}\">{1}</option>", item.Key, item.Value))
			.AppendLine($"</select>").ToString();   

What if we wanted to inspect the state of the stringbuilder in the middle of these chained expression. Is it possible to output state in such lengthy chained functional expressions? Yes, that is called the Tee method inside functional programming patterns. Other might call it for Tap such as used in Rx languages. The Tee method looks like this:

 
 
public static class FunctionalExtensions {

	public static T Tee<T>(this T @this, Action<T> act) {
		act(@this);
		return @this;
	}
	
}

 

We can now inspect state in the middle of chained expressions in functional expressions.

 
 
 
private static string BuildSelectBox(IDictionary<int, string> options, string id, bool includeUnknown) =>
		new StringBuilder()
			.AppendFormattedLine($"<select id=\"{id}\" name=\"{id}\">")
			.AppendWhen(() => includeUnknown, sb => sb.AppendLine("\t<option value=\"0\">Unknown</option>"))
            .Tee(Console.WriteLine)
			.AppendSequence(options, (sb, item) => sb.AppendFormattedLine("\t<option value=\"{0}\">{1}</option>", item.Key, item.Value))
			.AppendLine($"</select>").ToString();   
 
 

The picture below shows the output:

So there you have it, if you have lengthy chained functional expressions, make such a Tee helper method to peek into the state this far. The name Tee stems from the Unix Command by the same name. It copies contents from STDIN to STDOUT. More about Tee Unix command here:

https://shapeshed.com/unix-tee/

Functional programming - looking up current time and encapsulating usings

I looked at encapsulating Using statements today for functional programming and how to look up the current time with API available on the Internet.

public static class Disposable {
	
	public static TResult Using<TDisposable,TResult>(
		Func<TDisposable> factory,
		Func<TDisposable, TResult> map)		
		where TDisposable : IDisposable
	{
		using (var disposable = factory()){
			return map(disposable);
		}
		
	}	
}

void Main()
{
	var currentTime = EpochTime.AddSeconds(Disposable
			  .Using(() => new HttpClient(),
					client => JsonDocument.Parse(client.GetStringAsync(@"http://worldtimeapi.org/api/timezone/europe/oslo").Result))
			  .RootElement
			  .GetProperty("unixtime")
			 .GetInt64()).ToLocalTime(); //list of time zones available here: http://worldtimeapi.org/api/timezone
	currentTime.Dump("CurrentTime");	
}

public static DateTime EpochTime => new DateTime(1970, 1, 1);

The Disposable is abstracted away in the helper method called Using accepting a factory function to create a TDisposable that accepts an IDisposable. We look up the current time using the WorldTimeApi and make use of extracting the UnixTime which is measured from Epoch as the number of seconds elapsed from 1st January 1970. We make use of System.Text.Json here, which is part of .NET to parse the json retrieved.

Currying functions in C#

This article will look into helper methods for currying functions in C#. The definition of Currying consists of splitting up a function with multiple arguments into multiple functions accepting one argument. But you can also have some of the arguments provided via smaller functions, so be aware also of this alternative. What is in the name currying? The name has nothing to do with cooking from India, but comes from the mathematician Haskell Brooks Curry (!)

https://en.wikipedia.org/wiki/Haskell_Curry

A reason for introducing support for currying is that you can build complex functions from simpler functions as building blocks. Currying is explained great here:
https://www.c-sharpcorner.com/UploadFile/rmcochran/functional-programming-in-C-Sharp-currying/

We will see in the examples that we can provide multiple arguments at once and the syntax will look a bit special compared to other C# code. Curryings benefits is to allow a more flexible way to call a method. You can store into variables calls to a function providing a subset of argument and use that variable to either specify an intermediate other call or get the final result. Note - The function will be called when ALL arguments are provided ONCE ! This helps a lot of avoiding surprising side effects. Let's first look at a sample set of methods we want to support currying.

int FooFourArgs(string st, float x, int j, int k)
{
	Console.WriteLine($"Inside method FooFourArgs. Got parameters: st={st}, x={x}, j={j}, k={k}");
	return 42;
}

int FooThreeArgs(string st, float x, int j)
{
	Console.WriteLine($"Inside method FooThreeArgs. Got parameters: st={st}, x={x}, j={j}");
	return 42;
}

int FooTwoArgs(string st, float x)
{
	Console.WriteLine($"Inside method FooTwoArgs. Got parameters: st={st}, x={x}");
	return 41;
}

int FooOneArgs(string st)
{
	Console.WriteLine($"Inside method FooOneArgs. Got parameters: st={st}");
	return 40;
}

We want to call the sample methods above in a more flexible way by splitting the number of arguments we provide. Let's see the extension methods to call up to four arguments to a function. Note the use of chaining the lambda operator (=>) to provide the support for currying.

public static class FunctionExtensions
{
	public static Func<T1, TResult> Curried<T1, TResult>(this Func<T1, TResult> func)
	{
		return x1 => func(x1);
	}
	
	public static Func<T1, Func<T2, TResult>> Curried<T1, T2, TResult>(this Func<T1, T2, TResult> func)
	{
		return x1 => x2 => func(x1, x2);
	}

	public static Func<T1, Func<T2, Func<T3, TResult>>> Curried<T1, T2, T3, TResult>(this Func<T1, T2, T3, TResult> func)
	{
		return x1 => x2 => x3 => func(x1, x2, x3);
	}

	public static Func<T1, Func<T2, Func<T3, Func<T4, TResult>>>> Curried<T1, T2, T3, T4, TResult>(this Func<T1, T2, T3, T4, TResult> func)
	{
		return x1 => x2 => x3 => x4 => func(x1, x2, x3,x4);
	}
}

The following main method shows how to use these curry helper methods:

void Main()
{
	var curryOneArgsDelegate = new Func<string, int>((st) => FooOneArgs(st)).Curried();
	var curryOneArgsPhaseOne = curryOneArgsDelegate("hello");

	var curryTwoArgsDelegate = new Func<string, float, int>((st, x) => FooTwoArgs(st,x)).Curried();
	var curryTwoArgsPhaseOne = curryTwoArgsDelegate("hello");
	var curryTwoArgsPhaseTwo = curryTwoArgsPhaseOne(3.14f);

	var curryThreeArgsDelegate = new Func<string, float, int, int>((st, x, j) => FooThreeArgs(st, x, j)).Curried();
	var curryThreeArgsPhaseOne = curryThreeArgsDelegate("hello");
	var curryThreeArgsPhaseTwo = curryThreeArgsPhaseOne(3.14f);
	var curryThreeArgsPhaseThree = curryThreeArgsPhaseTwo(123);	
	//Or call currying in a single call passing in two or more parametres
	var curryThreeArgsPhaseOneToThree = curryThreeArgsDelegate("hello")(3.14f)(123);

	var curryFourArgsDelegate = new Func<string, float, int, int, int>((st, x, j, k) => FooFourArgs(st, x, j, k)).Curried();
	var curryFourArgsPhaseOne = curryFourArgsDelegate("hello");
	var curryFourArgsNextPhases = curryFourArgsPhaseOne(3.14f)(123)(456); //just pass in the last arguments if they are known at this stage
	curryFourArgsDelegate("hello")(3.14f)(123)(456); //you can pass in 1-4 parameters to FooFourArgs method - all in a single call for example or one by one
}

The output we get is this. Note that we only call the methods we defined when all parameters are sent in. The function call which had partial argument list provided did not result into a function call.

Inside method FooOneArgs. Got parameters: st=hello
Inside method FooTwoArgs. Got parameters: st=hello, x=3,14
Inside method FooThreeArgs. Got parameters: st=hello, x=3,14, j=123
Inside method FooThreeArgs. Got parameters: st=hello, x=3,14, j=123
Inside method FooFourArgs. Got parameters: st=hello, x=3,14, j=123, k=456

So from a higher level, currying a function f(x,y,z) means adding support that you could call the function like this:
f(x,g(y,z)) or f(x,g(y,h(z))) - there more arguments you get there is more variations of number of parameters and methods you can pass in. Here is another example how you can build up a calculation uing simpler methods.

void Main()
{
	Func Area = (x,y) => x*y;
	Func CubicArea = (x,y,z) => Area.Curried()(Area(x,y))(z);	
	CubicArea(3,2,4); //supplying all arguments manully is okay
}

CubicArea expects THREE arguments. The implementation allows us to use the Area function and via currying we can use that method and provide the last third argument avoiding compilation error. Currying makes your functions allow more flexible ways of being called.

Creating a data table from IEnumerable of T and defining column order explicitly in C#

This article shows code how you can create a DataTable from a collection of T (IEnumerable<T>) and defining explicitly the column order. An extension method for this looks like the following:

public static class DataTableExtensions
{


	public static DataTable CreateOrderedDataTable<T>(this IEnumerable<T> data)
	{
		var dataTable = new DataTable();
		var orderedProps = typeof(T).GetProperties(BindingFlags.Instance | BindingFlags.Public)
		 .OrderBy(prop => GetColumnOrder(prop)).ToList();
		
		foreach (var prop in orderedProps){
			dataTable.Columns.Add(prop.Name, Nullable.GetUnderlyingType(prop.PropertyType) ?? prop.PropertyType);
		}
		
		if (data != null)
		{
			dataTable.BeginLoadData();
			var enumerator = data.GetEnumerator();
			while (enumerator.MoveNext()){
			   var item = enumerator.Current;
			   var rowValues = new List<object>();
			   foreach (var prop in orderedProps){
			    rowValues.Add(prop.GetValue(item, null));		   	
			   }
			   dataTable.Rows.Add(rowValues.ToArray());			 			
			}
			dataTable.AcceptChanges();
		}
		return dataTable;
	}

	static int GetColumnOrder(PropertyInfo prop)
	{
		var displayAttribute = prop.GetCustomAttributes(typeof(DisplayAttribute), false).FirstOrDefault() as DisplayAttribute;
		int orderKey = displayAttribute?.Order ?? prop.MetadataToken;		
		return orderKey;
	}
	
}

We order first by DisplayAttribute and the Order value, and fallback to property's MetadataToken. This is an integer value that also returns the order the property was declared, in case you want to order just by the way properties are defined. We get the enumerator here and fetch the row one by one. We could use a simple foreach loop here too. Note the use of BeginLoadData and AcceptChanges. Consider the two classes next. One class does not set any explicit order, the other class uses the Display attribute's Order value to define a custom order of columns for the DataTable.

public class Car
{

	public int Id { get; set; }

	public string Make { get; set; }

	public string Model { get; set; }

	public string Color { get; set; }
}


public class CarV2
{
	[Display(Order = 4)]
	public int Id { get; set; }
	
	[Display(Order = 3)]
	public string Make { get; set; }
	
	[Display(Order = 2)]
	public string Model { get; set; }

	[Display(Order = 14)]
	public bool IsElectric { get; set; }

	[Display(Order = -188865)]
	public string Color { get; set; }
	
}

Next, the following little program in Linqpad tests this extension method and displays the datatables resulting with column ordering set.

void Main()
{
	var cars = new List<Car>{
		new Car { Id = 1, Make = "Audi", Model = "A5", Color = "Blue" },
		new Car { Id = 2, Make = "Volvo", Model = "XC60", Color = "Silver" },
		new Car { Id = 3, Make = "Golf", Model = "GTI", Color = "White" },
		new Car { Id = 4, Make = "Audi", Model = "A5", Color = "Blue" },
	};
	var dataTable = cars.CreateOrderedDataTable();
	dataTable.Dump("Cars datatable, data type is: Car");
	
	var carV2s = new List<CarV2>{
		new CarV2 { Id = 1, Make = "Audi", Model = "A5", Color = "Blue" },
		new CarV2 { Id = 2, Make = "Volvo", Model = "XC60", Color = "Silver" },
		new CarV2 { Id = 3, Make = "Golf", Model = "GTI", Color = "White" },
		new CarV2 { Id = 4, Make = "Audi", Model = "A5", Color = "Blue" },
	};	
	var dataTableV2 = carV2s.CreateOrderedDataTable();
	dataTableV2.Dump("Carsv2 datatable, datatype is CarV2");

}

Password hashing in .NET

This article will look on different ways to hash a password in .NET. MD5 was developed by Ron Rivest in 1991 and was used a lot in the 90s, but in 2005 it was revealed it contains collisions. MD5 and SHA-1 is not advised to used in sensitive hashing related to security anymore. Instead, a PBKDF or Password Derived Key-derivation function algorithm will be used. A PBKDF2-based method in Rfc2898DeriveBytes will be used. It has been available since .NET 6. Users of Asp.net Core Identity are recommended to use PasswordHasher instead : https://andrewlock.net/exploring-the-asp-net-core-identity-passwordhasher/ An overview of the arithmetic flow of PBKDF2 is shown below. In the diagram, SHA-512 is indicated, but the code shown in this article
uses SHA-256.

First off, to do a MD5 hash we can use the following :

 
 
 static string Md5(string input){
	using (var md5 = MD5.Create()){
		var byteHash = md5.ComputeHash(Encoding.UTF8.GetBytes(input)); 
		var hash = BitConverter.ToString(byteHash).Replace("-", "");
		return hash;
	}
}
 
 

And to test it out we can run the following:

 
 
 void Md5Demo()
{
	string inputPassword = "abc123";
	string md5Hash = Md5(inputPassword);
	Console.WriteLine("MD5 Demonstration in .NET");
	Console.WriteLine("-------------------------");
	Console.WriteLine($"Password to hash: {inputPassword}");
	Console.WriteLine($"MD5 hashed password: {md5Hash}");
	Console.WriteLine();
} 
 
 

MD5 Demonstration in .NET ------------------------- Password to hash: abc123 MD5 hashed password: E99A18C428CB38D5F260853678922E03 The MD5 hash above agrees with the online MD5 hash here: https://www.md5hashgenerator.com/ MD5 method here does not mention any salt, but this could be concatenated with the password to prevent against rainbow table attacks, that is dictionary attacks. Next, to perform PDKDF2 hashing, the code below can be used. Note that this algorithm will be run iteratively to generate a hash value that is increasingly more computationally expensive to calculate the hash of compared to the number of iterations and includes a salt, making it scalable
to be more and more difficult for attacks.

static byte[] _salt = RandomNumberGenerator.GetBytes(32);

static void HashPassword(string passwordToHash, int numberOfRounds)
{
	var sw = Stopwatch.StartNew();
	var hashedPassword = Rfc2898DeriveBytes.Pbkdf2(
		passwordToHash,
		_salt,
		numberOfRounds,
		HashAlgorithmName.SHA256,
		32);
	sw.Stop();

	Console.WriteLine();
	Console.WriteLine("Password to hash : " + passwordToHash);
	Console.WriteLine("Hashed Password : " + Convert.ToBase64String(hashedPassword));
	Console.WriteLine("Iterations < " + numberOfRounds + "> Elapsed Time: " + sw.ElapsedMilliseconds + " ms");
}

The value 32 here is the desired output length of the hash, we can decide how long the hash we get out of the call to the method. We can then test out the Pbkdf2 method using an increasing number of iterations.

 
 
 void RunPbkdf2HashDemo()
{
	const string passwordToHash = "abc123";

	Console.WriteLine("Password Based Key Derivation Function Demonstration in .NET");
	Console.WriteLine("------------------------------------------------------------");
	Console.WriteLine();
	Console.WriteLine("PBKDF2 Hashes using Rfc2898DeriveBytes");
	Console.WriteLine();

	HashPassword(passwordToHash, 1);
	HashPassword(passwordToHash, 10);
	HashPassword(passwordToHash, 100);
	HashPassword(passwordToHash, 1000);
	HashPassword(passwordToHash, 10000);
	HashPassword(passwordToHash, 100000);
	HashPassword(passwordToHash, 1000000);
	HashPassword(passwordToHash, 5000000);
} 
 
 

This gives the following output:

 
 
Password Based Key Derivation Function Demonstration in .NET
------------------------------------------------------------

PBKDF2 Hashes using Rfc2898DeriveBytes

Password to hash : abc123
Hashed Password : eqeul5z7l2dPrOo8WjH/oTt0RYHvlZ2lvk8SUoTjZq4=
Iterations (1) Elapsed Time: 0 ms

Password to hash : abc123
Hashed Password : wfd8qQobzBPZvdemqrtZczqctFe0JeAkKjU3IJ48cms=
Iterations (10) Elapsed Time: 0 ms

Password to hash : abc123
Hashed Password : VY45SxzhqjYronha0kt1mQx+JRDVlXj82prX3H7kjII=
Iterations (100) Elapsed Time: 0 ms

Password to hash : abc123
Hashed Password : B0LfHgRSslG/lWe7hbp4jb8dEqQ/bZwNtxsaqbVBZ2I=
Iterations (1000) Elapsed Time: 0 ms

Password to hash : abc123
Hashed Password : LAHwpS4bnbO7CQ1r7buYgUTrp10FyaRyeK6hCwGwv20=
Iterations (10000) Elapsed Time: 1 ms

Password to hash : abc123
Hashed Password : WDjyPySpULXtVOVmSR9cYlzAY4LWeJqDBhszKAfIaPc=
Iterations (100000) Elapsed Time: 13 ms

Password to hash : abc123
Hashed Password : sDx6sOrTl2b7cNZGUAecg7YO4Md/g3eAtfQSvh/vxpM=
Iterations (1000000) Elapsed Time: 127 ms

Password to hash : abc123
Hashed Password : ruywLaR0QApOU5bkqE/x2AAhYJzBj5y6D3P3IxlIF2I=
Iterations (5000000) Elapsed Time: 643 ms
 
 

Note that it takes many iterations before the computation takes significant time. Sources / links :

• OWASP recommends using 600 000 iterations when using SHA256 as noted here: https://cheatsheetseries.owasp.org/cheatsheets/Password_Storage_Cheat_Sheet.html#pbkdf2
• Hashing passwords using PBKDF2 are explained by Stephen Haunts here: Hashing Passwords Safely using a Password Based Key Derivation Function (PBKDF2)

AES Encryption with Galois Counter Mode (GCM) in C#

This article presents some helper methods for performing AES Encryption using Galois Counter Mode (GCM). AES or Advanced Encryption Standard is the most used encryption algorithm used today, having overtaken DES and Triple DES since 2001. We will look into the GCM mode of AES in this article. AES-GCM class AesGcm is supported in .NET Core 3.0 and newer .NET versions, plus in .NET Standard 2.1. AES-GCM is authenticated encryption, compared to default AES-CBC (Cipher Block Chaining). Benefits of using GCM mode of AES is the following:

• Data authenticity / integrity. This is provided via a tag that is outputted by the encryption and used while decrypting
• Provides support for sending additional data, used for example in newer TLS implementations to provide both encryption and a non-encrypted payload. This is called additional metadata

Here is a helper class to perform encryption and decryption using AES-GCM.

 
 public static class AesGcmEncryption {


	public static (byte[], byte[]) Encrypt(byte[] dataToEncrypt, byte[] key, byte[] nonce, byte[] associatedData = null)
	{
		using var aesGcm = new AesGcm(key);
		//tag and ciphertext will be filled during encryption
		var tag = new byte[16]; //tag is a hmac (hash-based message authentication code) to check that information has not been tampered with
	    var cipherText = new byte[dataToEncrypt.Length];
		aesGcm.Encrypt(nonce, dataToEncrypt, cipherText, tag, associatedData);
		return (cipherText, tag);
	}

	public static byte[] Decrypt(byte[] cipherText, byte[] key, byte[] nonce, byte[] tag, byte[] associatedData = null)
	{
		using var aesGcm = new AesGcm(key);
		//tag and ciphertext will be filled during encryption
		var decryptedData = new byte[cipherText.Length];
		aesGcm.Decrypt(nonce, cipherText, tag, decryptedData, associatedData);
		return decryptedData;
	}
	
}
 
 

In the code above, the encrypt method returns a tuple with the ciperText and the tag. These are the encrypted data and the tag, both must be used while decrypting and the tag provides as mentioned a means of checking the integrity of data, i.e. that data has not been tampered with. Note that the 16-byte tag and the ciphertext is filled after running the Encrypt method of the AesGcm class. The cipherText array must be the same length as the dataToEncrypt array inputted. Here is sample code to use AES-GCM. Note that the metadata used here, while optional, must match in case it is set in the encryption and decryption. The nonce must be 12 bytes - 96 bits in length.The nonce is similar to a initialization vector, although it is used once for the particular encryption and decryption, it is used to protect against replay attacks.

 
 
 void TestAesGCM()
{
	const string original = "Text to encrypt";
	var key = RandomNumberGenerator.GetBytes(32); //256 bits key
	var nonce = RandomNumberGenerator.GetBytes(12); //96 bits nonce
	
	(byte[] cipherText, byte[] tag) result = AesGcmEncryption.Encrypt(Encoding.UTF8.GetBytes(original),
	 key, nonce, Encoding.UTF8.GetBytes("some metadata 123"));
	 byte[] decryptedText = AesGcmEncryption.Decrypt(result.cipherText, key, nonce, result.tag, Encoding.UTF8.GetBytes("some metadata 123")); 
		
	Console.WriteLine("AES Encryption demo GCM - Galois Counter Mode:");
	Console.WriteLine("--------------");
	Console.WriteLine("Original Text = " + original);
	Console.WriteLine("Encrypted Text = " + Convert.ToBase64String(result.cipherText));
	Console.WriteLine("Tag = " + Convert.ToBase64String(result.tag));
	Console.WriteLine("Decrypted Text = " + Encoding.UTF8.GetString(decryptedText));
}
 
 

AES Encryption demo GCM - Galois Counter Mode: -------------- Original Text = Text to encrypt Encrypted Text = 9+2x0kctnRwiDDHBm0/H Tag = sSDxsg17HFdjE4cuqRlroQ== Decrypted Text = Text to encrypt Use AES-GCM to provide integrity checking and allowing to send in metadata if desired to encrypt and decrypting with the AES algorithm. We can protect the AES key using different methods, for example using the Data Protection API, this is only supported in Windows. Let's look at a helper class for using Data Protection API.

 
 
 public static class DataProtectionUtil {

	public static byte[] Protect(byte[] dataToEncrypt, byte[] optionalEntropy, DataProtectionScope scope)
	{
		var encryptedData = ProtectedData.Protect(dataToEncrypt, optionalEntropy, scope);
		return encryptedData;
	}
	
	public static byte[] Unprotect(byte[] encryptedData, byte[] optionalEntropy, DataProtectionScope scope){
		var decryptedData = ProtectedData.Unprotect(encryptedData, optionalEntropy, scope);
		return decryptedData;
	}

	public static string Protect(string dataToEncrypt, string optionalEntropy, DataProtectionScope scope)
	{
		var encryptedData = ProtectedData.Protect(Encoding.UTF8.GetBytes(dataToEncrypt), optionalEntropy != null ? Encoding.UTF8.GetBytes(optionalEntropy) : null, scope);
		return Convert.ToBase64String(encryptedData);
	}

	public static string Unprotect(string encryptedData, string optionalEntropy, DataProtectionScope scope)
	{
		var decryptedData = ProtectedData.Unprotect(Convert.FromBase64String(encryptedData), optionalEntropy != null ? Encoding.UTF8.GetBytes(optionalEntropy) : null, scope);
		return Encoding.UTF8.GetString(decryptedData);
	}

}
 
 

An example how to protect your AES key:

 
 
void EncryptAndDecryptWithProtectedKey(){
	var original = "Text to encrypt";
	Console.WriteLine($"Original Text = {original}");
	
	//Create key and nnoce . Encrypt our text with AES 
	var gcmKey = RandomNumberGenerator.GetBytes(32);
	var nonce = RandomNumberGenerator.GetBytes(12); 
	
	var result = EncryptText(original, gcmKey, nonce); 
	
	//Create some entropy and protect AES key
	var entropy = RandomNumberGenerator.GetBytes(16); 
	var protectedKey = ProtectedData.Protect(gcmKey, entropy, DataProtectionScope.CurrentUser);

	Console.WriteLine($"gcmKey = {Convert.ToBase64String(gcmKey)}, protectedKey = {Convert.ToBase64String(protectedKey)}");
	
	// Decrypt the text with AES. the AES key has to be retrieved with DPAPI.
	var decryptedText = DecryptText(result.encrypted, nonce, result.tag, protectedKey, entropy);

	Console.WriteLine($"Decrypted Text using AES GCM with key retrieved via Data Protection API = {decryptedText}");

}

private static (byte[] encrypted, byte[] tag) EncryptText(string original, byte[] gcmKey, byte[] nonce){
	return AesGcmEncryption.Encrypt(Encoding.UTF8.GetBytes(original), gcmKey, nonce, Encoding.UTF8.GetBytes("some meta"));
}

private static string DecryptText(byte[] encrypted, byte[] nonce, byte[] tag, byte[] protectedKey, byte[] entropy){
	
	var key = DataProtectionUtil.Unprotect(protectedKey, entropy, DataProtectionScope.CurrentUser);

	Console.WriteLine($"Inside DecryptText: gcmKey = {Convert.ToBase64String(key)}, protectedKey = {Convert.ToBase64String(protectedKey)}");

	var decryptedText = AesGcmEncryption.Decrypt(encrypted, key, nonce, tag, Encoding.UTF8.GetBytes("some meta"));
	return Encoding.UTF8.GetString(decryptedText);
}
 

Data Protection API is only supported on Windows platform, there are more possibilities to protect AES key but protecting your key is always a challenge when dealing with symmetric encryption algorithms such as AES. Some more links:

• What is a Nonce ? https://www.youtube.com/watch?v=EOgpr73-pgc
• AES Explained - https://www.youtube.com/watch?v=O4xNJsjtN6E
• Galois/Counter Mode (GCM) - https://www.youtube.com/watch?v=V2TlG3JbGp0
• Authenticated Encryption in .NET with AES-GCM - https://www.scottbrady91.com/c-sharp/aes-gcm-dotnet

Digital signatures with RSA in .NET

I have looked at Digital signatures with RSA in .NET today. Digital signatures are used to provide non-repudiation, an authenticity proof that the original sender is who the sender claims to be and also that the data has not been hampered with. We will return a tuple of both a SHA-256 computed hash of some document data and also its digital signature using the RSA algorithm. I have used .netstandard 2.0 here, so the code can be used in most frameworks in both .NET Framework and .NET. We will use RSA here to do the digital signature signing and verification. First off, here is a helper class to create a RSA encrypted signature of a SHA-256 hash, here we create a new RSA with key size 2048. RsaDigitalSignature.cs

 
 
 public class RsaDigitalSignature
{
	private RSA _rsa;


	public RsaDigitalSignature()
	{
		_rsa = RSA.Create();
		_rsa.KeySize = 2048;
	}
	
	public static byte[] ComputeHashSha256(byte[] toBeHashed)
	{
		using (var sha256 = SHA256.Create())
		{
			return sha256.ComputeHash(toBeHashed);
		}
	}

	public (byte[] Signature, byte[] HashOfData) SignData(byte[] dataToSign)
	{
		var hashOfDataToSign = ComputeHashSha256(dataToSign);
		return (_rsa.SignHash(
			hashOfDataToSign,
			HashAlgorithmName.SHA256,
			RSASignaturePadding.Pkcs1),
			hashOfDataToSign);
	}

	public bool VerifySignature(byte[] signature, byte[] hashOfDataToSign)
	{
		return _rsa.VerifyHash(hashOfDataToSign, signature, HashAlgorithmName.SHA256, RSASignaturePadding.Pkcs1);
	}

}
 
 
 

In the code above, we receive some document data and create the SHA-255 hash, which is computed. We return a tuple with the signed hash from the computed SHA-256 hash and also the computed SHA-256 hash itself. A console application that runs the sample code above is the following:

 
 
 void Main()
{
	SignAndVerifyData();
	//Console.ReadLine();
}

private static void SignAndVerifyData()
{
	Console.WriteLine("RSA-based sigital signature demo");
	var document = Encoding.UTF8.GetBytes("Document to sign");	
	var digitalSignature = new RsaDigitalSignature();
	var signature = digitalSignature.SignData(document);
	bool isValidSignature = digitalSignature.VerifySignature(signature.Signature, signature.HashOfData);
	Console.WriteLine($"\nInput Document:\n{Convert.ToBase64String(document)}\nIs the digital signature valid? {isValidSignature} \nSignature: {Convert.ToBase64String(signature.Signature)} \nHash of data:\n{ Convert.ToBase64String(signature.HashOfData)}");
}
 
 

Our verification of the signature shows that the verification of the digital signature passes.

 
Input Document:
RG9jdW1lbnQgdG8gc2lnbg==
Is the digital signature valid? True
Signature: Gok1x8Wxm9u5jTRcqrgPsI45ie3WPZLi/FNbaJMGTHqBmNbpJTEYjsXix97aIF6uPjgrxQWJKCegc8S4yASdut7TpJafO9wSRqvScc2SuOGK9BqnX+9GwRRQNti8ynm0ARRar+Z4hTpYY/XngFZ+ovvqIT3KRMK/7tsMmTg87mY0KelteFX7z7G7wPB9kKjT6ORYK4lVr35fihrbxei0XQP59YuEdALy+vbvKUm3JNv4sBU0lc9ZKpp2XF0rud8UnY1Nz4/XH7ZoaKfca5HXs9yq89DJRaPBRi1+Wv41vTCf8zFKPWZJrw6rm6kBMNHMENYbeBNdZyiCspTsHZmsVA==
Hash of data:
VPPxOVW2A38lCB810vuZbBH50KQaPSCouN0+tOpYDYs=
 

The code above uses a RSA created on the fly and is not so easy to share between a sender and a receiver. Let's look at how we can use X509 certificates to do the RSA encyption. It should be possible to share the source code below between the sender and the receiver and for example
export the public part of the X509 certificate to the receiver, which the receiver could install in a certificate store, only requred to know the thumbprint of the cert which is easy to see in MMC (Microsoft Management Console) or using Powershell and cd-ing into cert:\ folder . Let's first look at a helper class to get hold of a installed X509 certificate.

public class CertStoreUtil
{
	public static System.Security.Cryptography.X509Certificates.X509Certificate2 GetCertificateFromStore(
	System.Security.Cryptography.X509Certificates.StoreLocation storeLocation,
	string thumbprint, bool validOnly = true) {
	 var store = new X509Store(storeLocation);
	 store.Open(OpenFlags.ReadOnly);
	 var cert = store.Certificates.Find(X509FindType.FindByThumbprint, thumbprint, validOnly).FirstOrDefault();
	 store.Close();
	 return cert;
	}
}

Next up, a helper class to create a RSA-based digital signature like in the previous example, but using a certificate.

 
 
 public class RsaFromCertDigitalSignature
{
	private RSA _privateKey;
	private RSA _publicKey;

	public RsaFromCertDigitalSignature(StoreLocation storeLocation, string thumbprint)
	{
		_privateKey = CertStoreUtil.GetCertificateFromStore(StoreLocation.LocalMachine, thumbprint).GetRSAPrivateKey();
		_publicKey = CertStoreUtil.GetCertificateFromStore(StoreLocation.LocalMachine, thumbprint).GetRSAPrivateKey();
	}

	public static byte[] ComputeHashSha256(byte[] toBeHashed)
	{
		using (var sha256 = SHA256.Create())
		{
			return sha256.ComputeHash(toBeHashed);
		}
	}

	public (byte[] Signature, byte[] HashOfData) SignData(byte[] dataToSign)
	{
		var hashOfDataToSign = ComputeHashSha256(dataToSign);
		return (_privateKey.SignHash(
			hashOfDataToSign,
			HashAlgorithmName.SHA256,
			RSASignaturePadding.Pkcs1),
			hashOfDataToSign);
	}

	public bool VerifySignature(byte[] signature, byte[] hashOfDataToSign)
	{
		return _publicKey.VerifyHash(hashOfDataToSign, signature, HashAlgorithmName.SHA256, RSASignaturePadding.Pkcs1);
	}

}

 
 

A console app that tests out the code above is shown next, I have selected a random cert on my dev pc here.

 
 
 void Main()
{
	SignAndVerifyData();
	//Console.ReadLine();
}

private static void SignAndVerifyData()
{
	Console.WriteLine("RSA-based sigital signature demo");
	var document = Encoding.UTF8.GetBytes("Document to sign");

	//var x509CertLocalHost = CertStoreUtil.GetCertificateFromStore(StoreLocation.LocalMachine, "1f0b749ff936abddad89f4bbea7c30ed64e3dd07");
		
	var digitalSignatureWithCert = new RsaFromCertDigitalSignature(StoreLocation.LocalMachine, "1f0b749ff936abddad89f4bbea7c30ed64e3dd07");
	var signatureWithCert = digitalSignatureWithCert.SignData(document);
	bool isValidSignatureFromCert = digitalSignatureWithCert.VerifySignature(signatureWithCert.Signature, signatureWithCert.HashOfData);
    Console.WriteLine(
		$@"Input Document:
		{Convert.ToBase64String(document)}
		Is the digital signature signed with private key of CERT valid according to public key of CERT? {isValidSignatureFromCert}
		Signature: {Convert.ToBase64String(signatureWithCert.Signature)} 
		Hash of data:\n{Convert.ToBase64String(signatureWithCert.HashOfData)}");
}

 
 

Now here is an important concept in digital signatures :

• For digital signatures, we MUST use a private key (e.g. private key of RSA instance, which can either be made on the fly or retrieved from for example a X509 certificate. Or a Json web key in a more modern example.
• For digital signature, to verify a signature we can use either the public or the private key, usually just the public key (which can be shared). For X509 certiifcates, we usually share a public cert (.cert or similar format) and keep our private cert ourselves (.pfx).

Sample output of the console app shown above:

 
 RSA-based sigital signature demo
Input Document:
    RG9jdW1lbnQgdG8gc2lnbg==
    Is the digital signature signed with private key of CERT valid according to public key of CERT? True
    Signature: ZHWzJeZnwbfI109uK0T4ubq4B+CHedQPIDgPREz+Eq9BR6A9y6kQEvSrxqUHvOppSDN5kDt5bTiWv1pvDPow+czb7N6kmFf1zQUxUs3ip4WPovBtQKmfpf9/i3DNkRILcoMLdZdKnn0aSaK66f0oxkSIc4nEkb3O9PbejVso6wLqSdDCh96d71gbHqOjyiZLBj2VlqalWvEPuo9GB0s2Uz2fxtFGMUQiZvH3jKR+9F4LwvKCc1K0E/+J4Np57JSfKgmid9QyL2r7nO19SVoVL3yBY7D8UxVIRw8sT/+JKXlnyh8roK7kaxDtW4+FMK6LT/QPvi8LkiNmA+eVv3kk9w==
    Hash of data:\nVPPxOVW2A38lCB810vuZbBH50KQaPSCouN0+tOpYDYs=
 

Using synthesized speech in Azure Cognitive Services - Text to Speech

I have extended my demo repo with Multi-Lingual translator to include AI realistic speech. The Github repo for the demo is available here :

https://github.com/toreaurstadboss/MultiLingual.Translator

The speech synthesis service of Azure AI is accessed via a REST service. You can actually test it out first in Postman, retrieving an access token via an endpoint for this and then calling the text to speech endpoint using the access token as a bearer token. To get the demo working, you have to inside the Azure Portal create the necessary resources / services. This article is focused on speech service. Important, if you want to test out the DEMO yourself, remember to put the keys into environment variables so they are not exposed via source control. To get started with speech synthesis in Azure Cognitive Services, add a Speech Service resource via the Azure Portal. https://learn.microsoft.com/en-us/azure/ai-services/speech-service/overview We also need to add audio capability to our demo, which is a .NET MAUI Blazor app. The Nuget package used is the following : MultiLingual.Translator.csproj

<ItemGroup>
	<PackageReference Include="Plugin.Maui.Audio" Version="2.0.0" />
</ItemGroup>

This Nuget package's website is here: https://github.com/jfversluis/Plugin.Maui.Audio The MauiProgram.cs looks like the following, make note of AudioManager.Current, which is registered as a singleton. MauiProgram.cs

using Microsoft.Extensions.Configuration;
using MultiLingual.Translator.Lib;
using Plugin.Maui.Audio;

namespace MultiLingual.Translator;

public static class MauiProgram
{
	public static MauiApp CreateMauiApp()
	{
		var builder = MauiApp.CreateBuilder();
		builder
			.UseMauiApp<App>()
			.ConfigureFonts(fonts =>
			{
				fonts.AddFont("OpenSans-Regular.ttf", "OpenSansRegular");
			});

		builder.Services.AddMauiBlazorWebView();
		#if DEBUG
		builder.Services.AddBlazorWebViewDeveloperTools();
#endif

		builder.Services.AddSingleton(AudioManager.Current);
		builder.Services.AddTransient<MainPage>();

		builder.Services.AddScoped<IDetectLanguageUtil, DetectLanguageUtil>();
        builder.Services.AddScoped<ITranslateUtil, TranslateUtil>();
		builder.Services.AddScoped<ITextToSpeechUtil, TextToSpeechUtil>();

		var config = new ConfigurationBuilder().AddJsonFile("appsettings.json").Build();
		builder.Configuration.AddConfiguration(config);

        return builder.Build();
	}
}

Next up, let's look at the TextToSpeechUtil. This class, which is a service that does two things against the REST API of the text-to-speech Azure Cognitive AI service :

1. Fetch an access token
2. Synthesize text to speech

TextToSpeechUtil.cs

using Microsoft.Extensions.Configuration;
using MultiLingual.Translator.Lib.Models;
using System.Security;
using System.Text;

namespace MultiLingual.Translator.Lib
{
    public class TextToSpeechUtil : ITextToSpeechUtil
    {

        public TextToSpeechUtil(IConfiguration configuration)
        {
            _configuration = configuration;
        }

        public async Task<TextToSpeechResult> GetSpeechFromText(string text, string language, TextToSpeechLanguage[] actorVoices, string? preferredVoiceActorId)
        {
            var result = new TextToSpeechResult();

            result.Transcript = GetSpeechTextXml(text, language, actorVoices, preferredVoiceActorId, result);
            result.ContentType = _configuration[TextToSpeechSpeechContentType];
            result.OutputFormat = _configuration[TextToSpeechSpeechXMicrosoftOutputFormat];
            result.UserAgent = _configuration[TextToSpeechSpeechUserAgent];
            result.AvailableVoiceActorIds = ResolveAvailableActorVoiceIds(language, actorVoices);
            result.LanguageCode = language;

            string? token = await GetUpdatedToken();

            HttpClient httpClient = GetTextToSpeechWebClient(token);

            string ttsEndpointUrl = _configuration[TextToSpeechSpeechEndpoint];
            var response = await httpClient.PostAsync(ttsEndpointUrl, new StringContent(result.Transcript, Encoding.UTF8, result.ContentType));

            using (var memStream = new MemoryStream()) {
                var responseStream = await response.Content.ReadAsStreamAsync();
                responseStream.CopyTo(memStream);
                result.VoiceData = memStream.ToArray();
            }

            return result;
        }

        private async Task<string?> GetUpdatedToken()
        {
            string? token = _token?.ToNormalString();
            if (_lastTimeTokenFetched == null || DateTime.Now.Subtract(_lastTimeTokenFetched.Value).Minutes > 8)
            {
                token = await GetIssuedToken();
            }

            return token;
        }

        private HttpClient GetTextToSpeechWebClient(string? token)
        {
            var httpClient = new HttpClient();
            httpClient.DefaultRequestHeaders.Authorization = new System.Net.Http.Headers.AuthenticationHeaderValue("Bearer", token);
            httpClient.DefaultRequestHeaders.Add("X-Microsoft-OutputFormat", _configuration[TextToSpeechSpeechXMicrosoftOutputFormat]);
            httpClient.DefaultRequestHeaders.Add("User-Agent", _configuration[TextToSpeechSpeechUserAgent]);
            return httpClient;
        }
       
        private string GetSpeechTextXml(string text, string language, TextToSpeechLanguage[] actorVoices, string? preferredVoiceActorId, TextToSpeechResult result)
        {
            result.VoiceActorId = ResolveVoiceActorId(language, preferredVoiceActorId, actorVoices);
            string speechXml = $@"
            <speak version='1.0' xml:lang='en-US'>
                <voice xml:lang='en-US' xml:gender='Male' name='Microsoft Server Speech Text to Speech Voice {result.VoiceActorId}'>
                    <prosody rate='1'>{text}</prosody>
                </voice>
            </speak>";
            return speechXml;               
        }

        private List<string> ResolveAvailableActorVoiceIds(string language, TextToSpeechLanguage[] actorVoices)
        {
            if (actorVoices?.Any() == true)
            {
                var voiceActorIds = actorVoices.Where(v => v.LanguageKey == language || v.LanguageKey.Split("-")[0] == language).SelectMany(v => v.VoiceActors).Select(v => v.VoiceId).ToList();
                return voiceActorIds;
            }
            return new List<string>();
        }

        private string ResolveVoiceActorId(string language, string? preferredVoiceActorId, TextToSpeechLanguage[] actorVoices)
        {
            string actorVoiceId = "(en-AU, NatashaNeural)"; //default to a select voice actor id 
            if (actorVoices?.Any() == true)
            {
                var voiceActorsForLanguage = actorVoices.Where(v => v.LanguageKey == language || v.LanguageKey.Split("-")[0] == language).SelectMany(v => v.VoiceActors).Select(v => v.VoiceId).ToList();
                if (voiceActorsForLanguage != null)
                {
                    if (voiceActorsForLanguage.Any() == true)
                    {
                        var resolvedPreferredVoiceActorId = voiceActorsForLanguage.FirstOrDefault(v => v == preferredVoiceActorId);
                        if (!string.IsNullOrWhiteSpace(resolvedPreferredVoiceActorId))
                        {
                            return resolvedPreferredVoiceActorId!;
                        }
                        actorVoiceId = voiceActorsForLanguage.First();
                    }
                }
            }
            return actorVoiceId;
        }

        private async Task<string> GetIssuedToken()
        {
            var httpClient = new HttpClient();
            string? textToSpeechSubscriptionKey = Environment.GetEnvironmentVariable("AZURE_TEXT_SPEECH_SUBSCRIPTION_KEY", EnvironmentVariableTarget.Machine);
            httpClient.DefaultRequestHeaders.Add(OcpApiSubscriptionKeyHeaderName, textToSpeechSubscriptionKey);
            string tokenEndpointUrl = _configuration[TextToSpeechIssueTokenEndpoint];
            var response = await httpClient.PostAsync(tokenEndpointUrl, new StringContent("{}"));
            _token = (await response.Content.ReadAsStringAsync()).ToSecureString();
            _lastTimeTokenFetched = DateTime.Now;
            return _token.ToNormalString();
        }

        private const string OcpApiSubscriptionKeyHeaderName = "Ocp-Apim-Subscription-Key";
        private const string TextToSpeechIssueTokenEndpoint = "TextToSpeechIssueTokenEndpoint";
        private const string TextToSpeechSpeechEndpoint = "TextToSpeechSpeechEndpoint";        
        private const string TextToSpeechSpeechContentType = "TextToSpeechSpeechContentType";
        private const string TextToSpeechSpeechUserAgent = "TextToSpeechSpeechUserAgent";
        private const string TextToSpeechSpeechXMicrosoftOutputFormat = "TextToSpeechSpeechXMicrosoftOutputFormat";

        private readonly IConfiguration _configuration;

        private DateTime? _lastTimeTokenFetched = null;
        private SecureString _token = null;

    }
}

Let's look at the appsettings.json file. The Ocp-Apim-Subscription-Key is put into environment variable, this is a secret key you do not want to expose to avoid leaking a key an running costs for usage of service. Appsettings.json

{
  "TextToSpeechIssueTokenEndpoint": "https://norwayeast.api.cognitive.microsoft.com/sts/v1.0/issuetoken",
  "TextToSpeechSpeechEndpoint": "https://norwayeast.tts.speech.microsoft.com/cognitiveservices/v1",
  "TextToSpeechSpeechContentType": "application/ssml+xml",
  "TextToSpeechSpeechUserAgent": "MultiLingualTranslatorBlazorDemo",
  "TextToSpeechSpeechXMicrosoftOutputFormat": "audio-24khz-48kbitrate-mono-mp3"
}

Next up, I have gathered all the voice actor ids for languages in Azure Cognitive Services which have voice actor ids. Thesee are all the most known languages in the list of Azure about 150 supported languages, see the following json for an overview of voice actor ids. For example, Norwegian language got three voice actors that are synthesized neural net trained AI voice actors for realistic speech synthesis.

       [
  {
    "LanguageKey": "af-ZA",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "af-ZA-AdriNeural2",
        "VoiceId": "(af-ZA, AdriNeural2)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "af-ZA-WillemNeural2",
        "VoiceId": "(af-ZA, WillemNeural2)"
      }
    ]
  },
  {
    "LanguageKey": "am-ET",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "am-ET-MekdesNeural2",
        "VoiceId": "(am-ET, MekdesNeural2)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "am-ET-AmehaNeural2",
        "VoiceId": "(am-ET, AmehaNeural2)"
      }
    ]
  },
  {
    "LanguageKey": "ar-AE",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "ar-AE-FatimaNeural",
        "VoiceId": "(ar-AE, FatimaNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "ar-AE-HamdanNeural",
        "VoiceId": "(ar-AE, HamdanNeural)"
      }
    ]
  },
  {
    "LanguageKey": "ar-BH",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "ar-BH-LailaNeural",
        "VoiceId": "(ar-BH, LailaNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "ar-BH-AliNeural",
        "VoiceId": "(ar-BH, AliNeural)"
      }
    ]
  },
  {
    "LanguageKey": "ar-DZ",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "ar-DZ-AminaNeural",
        "VoiceId": "(ar-DZ, AminaNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "ar-DZ-IsmaelNeural",
        "VoiceId": "(ar-DZ, IsmaelNeural)"
      }
    ]
  },
  {
    "LanguageKey": "ar-EG",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "ar-EG-SalmaNeural",
        "VoiceId": "(ar-EG, SalmaNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "ar-EG-ShakirNeural",
        "VoiceId": "(ar-EG, ShakirNeural)"
      }
    ]
  },
  {
    "LanguageKey": "ar-IQ",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "ar-IQ-RanaNeural",
        "VoiceId": "(ar-IQ, RanaNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "ar-IQ-BasselNeural",
        "VoiceId": "(ar-IQ, BasselNeural)"
      }
    ]
  },
  {
    "LanguageKey": "ar-JO",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "ar-JO-SanaNeural",
        "VoiceId": "(ar-JO, SanaNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "ar-JO-TaimNeural",
        "VoiceId": "(ar-JO, TaimNeural)"
      }
    ]
  },
  {
    "LanguageKey": "ar-KW",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "ar-KW-NouraNeural",
        "VoiceId": "(ar-KW, NouraNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "ar-KW-FahedNeural",
        "VoiceId": "(ar-KW, FahedNeural)"
      }
    ]
  },
  {
    "LanguageKey": "ar-LB",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "ar-LB-LaylaNeural",
        "VoiceId": "(ar-LB, LaylaNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "ar-LB-RamiNeural",
        "VoiceId": "(ar-LB, RamiNeural)"
      }
    ]
  },
  {
    "LanguageKey": "ar-LY",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "ar-LY-ImanNeural",
        "VoiceId": "(ar-LY, ImanNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "ar-LY-OmarNeural",
        "VoiceId": "(ar-LY, OmarNeural)"
      }
    ]
  },
  {
    "LanguageKey": "ar-MA",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "ar-MA-MounaNeural",
        "VoiceId": "(ar-MA, MounaNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "ar-MA-JamalNeural",
        "VoiceId": "(ar-MA, JamalNeural)"
      }
    ]
  },
  {
    "LanguageKey": "ar-OM",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "ar-OM-AyshaNeural",
        "VoiceId": "(ar-OM, AyshaNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "ar-OM-AbdullahNeural",
        "VoiceId": "(ar-OM, AbdullahNeural)"
      }
    ]
  },
  {
    "LanguageKey": "ar-QA",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "ar-QA-AmalNeural",
        "VoiceId": "(ar-QA, AmalNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "ar-QA-MoazNeural",
        "VoiceId": "(ar-QA, MoazNeural)"
      }
    ]
  },
  {
    "LanguageKey": "ar-SA",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "ar-SA-ZariyahNeural",
        "VoiceId": "(ar-SA, ZariyahNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "ar-SA-HamedNeural",
        "VoiceId": "(ar-SA, HamedNeural)"
      }
    ]
  },
  {
    "LanguageKey": "ar-SY",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "ar-SY-AmanyNeural",
        "VoiceId": "(ar-SY, AmanyNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "ar-SY-LaithNeural",
        "VoiceId": "(ar-SY, LaithNeural)"
      }
    ]
  },
  {
    "LanguageKey": "ar-TN",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "ar-TN-ReemNeural",
        "VoiceId": "(ar-TN, ReemNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "ar-TN-HediNeural",
        "VoiceId": "(ar-TN, HediNeural)"
      }
    ]
  },
  {
    "LanguageKey": "ar-YE",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "ar-YE-MaryamNeural",
        "VoiceId": "(ar-YE, MaryamNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "ar-YE-SalehNeural",
        "VoiceId": "(ar-YE, SalehNeural)"
      }
    ]
  },
  {
    "LanguageKey": "az-AZ",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "az-AZ-BanuNeural2",
        "VoiceId": "(az-AZ, BanuNeural2)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "az-AZ-BabekNeural2",
        "VoiceId": "(az-AZ, BabekNeural2)"
      }
    ]
  },
  {
    "LanguageKey": "bg-BG",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "bg-BG-KalinaNeural",
        "VoiceId": "(bg-BG, KalinaNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "bg-BG-BorislavNeural",
        "VoiceId": "(bg-BG, BorislavNeural)"
      }
    ]
  },
  {
    "LanguageKey": "bn-BD",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "bn-BD-NabanitaNeural2",
        "VoiceId": "(bn-BD, NabanitaNeural2)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "bn-BD-PradeepNeural2",
        "VoiceId": "(bn-BD, PradeepNeural2)"
      }
    ]
  },
  {
    "LanguageKey": "bn-IN",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "bn-IN-TanishaaNeural2",
        "VoiceId": "(bn-IN, TanishaaNeural2)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "bn-IN-BashkarNeural2",
        "VoiceId": "(bn-IN, BashkarNeural2)"
      }
    ]
  },
  {
    "LanguageKey": "bs-BA",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "bs-BA-VesnaNeural2",
        "VoiceId": "(bs-BA, VesnaNeural2)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "bs-BA-GoranNeural2",
        "VoiceId": "(bs-BA, GoranNeural2)"
      }
    ]
  },
  {
    "LanguageKey": "ca-ES",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "ca-ES-JoanaNeural",
        "VoiceId": "(ca-ES, JoanaNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "ca-ES-EnricNeural",
        "VoiceId": "(ca-ES, EnricNeural)"
      },
      {
        "IsFemale": true,
        "VoiceActor": "ca-ES-AlbaNeural",
        "VoiceId": "(ca-ES, AlbaNeural)"
      }
    ]
  },
  {
    "LanguageKey": "cs-CZ",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "cs-CZ-VlastaNeural",
        "VoiceId": "(cs-CZ, VlastaNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "cs-CZ-AntoninNeural",
        "VoiceId": "(cs-CZ, AntoninNeural)"
      }
    ]
  },
  {
    "LanguageKey": "cy-GB",
    "VoiceActors": [
      {
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      {
        "IsFemale": true,
        "VoiceActor": "zh-CN-XiaoyanNeural",
        "VoiceId": "(zh-CN, XiaoyanNeural)"
      },
      {
        "IsFemale": true,
        "VoiceActor": "zh-CN-XiaoyouNeural",
        "VoiceId": "(zh-CN, XiaoyouNeural)"
      },
      {
        "IsFemale": true,
        "VoiceActor": "zh-CN-XiaozhenNeural",
        "VoiceId": "(zh-CN, XiaozhenNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "zh-CN-YunfengNeural",
        "VoiceId": "(zh-CN, YunfengNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "zh-CN-YunhaoNeural",
        "VoiceId": "(zh-CN, YunhaoNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "zh-CN-YunxiaNeural",
        "VoiceId": "(zh-CN, YunxiaNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "zh-CN-YunyeNeural",
        "VoiceId": "(zh-CN, YunyeNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "zh-CN-YunzeNeural",
        "VoiceId": "(zh-CN, YunzeNeural)"
      },
      {
        "IsFemale": true,
        "VoiceActor": "zh-CN-XiaorouNeural1",
        "VoiceId": "(zh-CN, XiaorouNeural1)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "zh-CN-YunjieNeural1",
        "VoiceId": "(zh-CN, YunjieNeural1)"
      }
    ]
  },
  {
    "LanguageKey": "zh-CN-GUANGXI",
    "VoiceActors": [
      {
        "IsFemale": false,
        "VoiceActor": "zh-CN-guangxi-YunqiNeural1,2",
        "VoiceId": "(zh-CN, guangxi)"
      }
    ]
  },
  {
    "LanguageKey": "zh-CN-henan",
    "VoiceActors": [
      {
        "IsFemale": false,
        "VoiceActor": "zh-CN-henan-YundengNeural2",
        "VoiceId": "(zh-CN, henan)"
      }
    ]
  },
  {
    "LanguageKey": "zh-CN-liaoning",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "zh-CN-liaoning-XiaobeiNeural1,2",
        "VoiceId": "(zh-CN, liaoning)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "zh-CN-liaoning-YunbiaoNeural1,2",
        "VoiceId": "(zh-CN, liaoning)"
      }
    ]
  },
  {
    "LanguageKey": "zh-CN-shaanxi",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "zh-CN-shaanxi-XiaoniNeural1,2",
        "VoiceId": "(zh-CN, shaanxi)"
      }
    ]
  },
  {
    "LanguageKey": "zh-CN-shandong",
    "VoiceActors": [
      {
        "IsFemale": false,
        "VoiceActor": "zh-CN-shandong-YunxiangNeural2",
        "VoiceId": "(zh-CN, shandong)"
      }
    ]
  },
  {
    "LanguageKey": "zh-CN-sichuan",
    "VoiceActors": [
      {
        "IsFemale": false,
        "VoiceActor": "zh-CN-sichuan-YunxiNeural1,2",
        "VoiceId": "(zh-CN, sichuan)"
      }
    ]
  },
  {
    "LanguageKey": "zh-HK",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "zh-HK-HiuMaanNeural",
        "VoiceId": "(zh-HK, HiuMaanNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "zh-HK-WanLungNeural",
        "VoiceId": "(zh-HK, WanLungNeural)"
      },
      {
        "IsFemale": true,
        "VoiceActor": "zh-HK-HiuGaaiNeural",
        "VoiceId": "(zh-HK, HiuGaaiNeural)"
      }
    ]
  },
  {
    "LanguageKey": "zh-TW",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "zh-TW-HsiaoChenNeural",
        "VoiceId": "(zh-TW, HsiaoChenNeural)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "zh-TW-YunJheNeural",
        "VoiceId": "(zh-TW, YunJheNeural)"
      },
      {
        "IsFemale": true,
        "VoiceActor": "zh-TW-HsiaoYuNeural",
        "VoiceId": "(zh-TW, HsiaoYuNeural)"
      }
    ]
  },
  {
    "LanguageKey": "zu-ZA",
    "VoiceActors": [
      {
        "IsFemale": true,
        "VoiceActor": "zu-ZA-ThandoNeural2",
        "VoiceId": "(zu-ZA, ThandoNeural2)"
      },
      {
        "IsFemale": false,
        "VoiceActor": "zu-ZA-ThembaNeural2",
        "VoiceId": "(zu-ZA, ThembaNeural2)"
      }
    ]
  }
]
               
                  

Let's look at the source code for calling the TextToSpeechUtil.cs shown above from a MAUI Blazor app view, Index.razor The code below shown is two private methods that does the work of retrieving the audio file from the Azure Speeech Service by first loading up all the voice actor ids from a bundled json file of voice actors displayed above and deserialize this into a list of voice actors. Retrieving the audio file passes in the translated text of which to generate synthesized speedch for and also the target language, all available actor voices and preferred voice actor id, if set. Retrieved is metadata and the audio file, in a MP3 file format. The file format is recognized by for example Windows withouth having to have any codec libraries installed in addition. Index.razor (Inside the @code block { .. } of that razor file)

 private async Task<TextToSpeechLanguage[]> GetActorVoices()
    {
        //https://learn.microsoft.com/en-us/azure/ai-services/speech-service/language-support?tabs=tts
        Stream actorVoicesStream = await FileSystem.OpenAppPackageFileAsync("voicebook.json");
        using StreamReader sr = new StreamReader(actorVoicesStream);
        string actorVoicesJson = string.Empty;
        string line;

        while ((line = sr.ReadLine()) != null)
        {
            //Console.WriteLine(line);
            actorVoicesJson += line;
        }

        var actorVoices = JsonSerializer.Deserialize<TextToSpeechLanguage[]>(actorVoicesJson);
        return actorVoices;
    }

    private async void SpeakText()
    {
        await Submit();
        var actorVoices = await GetActorVoices();
        TextToSpeechResult textToSpeechResult = await TextToSpeechUtil.GetSpeechFromText(Model.TranslatedText, Model.TargetLanguage, actorVoices, Model.PreferredVoiceActorId);

        Model.ActiveVoiceActorId = textToSpeechResult.VoiceActorId;
        Model.Transcript = textToSpeechResult.Transcript;
        Model.AvailableVoiceActorIds = textToSpeechResult.AvailableVoiceActorIds;
        Model.AdditionalVoiceDataMetaInformation = $"Byte size voice data: {textToSpeechResult?.VoiceData?.Length}, Audio output format: {textToSpeechResult.OutputFormat}";

        var voiceFolder = Path.Combine(FileSystem.Current.AppDataDirectory, "Resources", "Raw");
        if (!Directory.Exists(voiceFolder))
        {
            Directory.CreateDirectory(voiceFolder);
        }
        string voiceFile = "textToSpeechVoiceOutput_" + Model.TargetLanguage + Guid.NewGuid().ToString("N") + ".mpga";
        string voiceRelativeFile = Path.Combine(voiceFile);

        string voiceFileFullPath = Path.Combine(voiceFolder, voiceFile);
        await File.WriteAllBytesAsync(voiceFileFullPath, textToSpeechResult.VoiceData);
        Stream voiceStream = File.OpenRead(voiceFileFullPath);

        StateHasChanged();

        var player = AudioManager.CreatePlayer(voiceStream);
        player.Play();
    }

A screenshot shows how the DEMO app now looks like. You can translate text into other language and then have speech synthesis in Azure AI Cognitive Service generate a realistic audio speech of the translated text so you can also see how the text not only is translated, but also pronounced.