Overview of symmetric encryption in .NET

Introduction

A symmetric encryption algorithm is one where the cryptographic key is the same for both encryption and decryption and is shared among the parties involved in the process.

Ideally only a small group of reliable people should have access to this key. Attackers decipher an encrypted message rather than trying to defeat the algorithm itself. The key can vary in size so the attacker will need to know this first. Once they know this then they will try combinations of possible key characters.

A clear disadvantage with this approach is that distributing and storing keys in a safe and reliable manner is difficult. On the other hand symmetric algorithms are fast.

In this short overview we’ll look at the symmetric encryption algorithms currently supported in .NET.

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An overview of digital signatures in .NET

Introduction

A digital signature in software has about the same role as a real signature on a document. It proves that a certain person has signed the document thereby authenticating it. A signature increases security around a document for both parties involved, i.e. the one who signed the document – the signee – and the one that uses the document afterwards. The one who signed can claim that the document belongs to them, i.e. it originates from them and cannot be used by another person. If you sign a bank loan request then you should receive the loan and not someone else. Also, the party that takes the document for further processing can be sure that it really originates from the person who signed it. The signee cannot claim that the signature belongs to some other person and they have nothing to do with the document. This latter is called non-repudiation. The signee cannot deny that the document originates from him or her.

Digital signatures in software are used to enhance messaging security. The receiver must be able to know for sure that the message originated with one specific sender and that the sender cannot claim that it was someone else who sent the message. While it is quite possible to copy someone’s signature on a paper document it is much harder to forge a strong digital signature.

In this post we’ll review how digital signatures are implemented in .NET

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Sharing numeric values across threads using Java 8 LongAdder

In this post we saw how to share primitive values across threads using the various atomic objects in the java.util.concurrent.atomic package. The example code demonstrated the AtomicInteger object which is the thread-safe variant of a “normal” integer. Mathematical operations like adding a value to an integer are carried out atomically for that object. This means that the low-level instructions involved in adding two integers are carried out as one unit without the risk of another interfering thread. The same package includes atomic versions of other primitive values such as AtomicBoolean or AtomicLong.

In this post we’ll take a quick look at an addition in Java 8 relevant to sharing integers, longs and doubles.

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Sharing primitives across threads in Java using atomic objects

Threading and parallel execution are popular choices when making applications more responsive and resource-efficient. Various tasks are carried out on separate threads where they either produce some result relevant to the main thread or just run in the background “unnoticed”. Often these tasks work autonomously meaning they have their own set of dependencies and variables. That is they do not interfere with a resource that is common to 2 or more threads.

However, that’s not always the case. Imagine that multiple threads are trying to update the same primitive like an integer counter. They perform some action and then update this counter. In this post we’ll see what can go wrong.

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Getting the result of the first completed parallel task in Java

In this post we saw how to delegate one or more parallel tasks to different threads and wait for all of them to complete. We pretended that 4 different computations took 1,2,3 and respectively 4 seconds to complete. If we execute each calculation one after the other on the same thread then it takes 10 seconds to complete them all. We can do a lot better by assigning each operation to a separate thread and let them run in parallel. The Future and Callable of T objects along with a thread pool make this very easy to implement.

There are situations where we only need the result from 1 parallel operation. Imagine that it’s enough to complete 1 of the four computations in the example code so that our main thread can continue. We don’t know how long each operation will take so we let them have a race. The one that is executed first returns its value and the rest are interrupted and forgotten. We’ll see how to achieve that in this post.

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Getting a result from a parallel task in Java using CompletableFuture

In this post we saw how to start several processes on different threads using the CompletableFuture class. The example concentrated on methods with no return value. We let CompletableFuture finish the tasks in parallel before continuing with another process.

In this post we’ll see a usage of CompletableFuture for functions with a return value. We’ll reuse several elements we saw in the post that concentrated on the Future class.

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Running a task on a different thread in Java 8

Occasionally it can be worth putting a task on a different thread so that it doesn’t block the main thread. Examples include a task that analyses heavy files, a task that sends out emails etc. If we put these tasks on a different thread and don’t wait for it to return a result then it’s called the fire-and-forget pattern. We start a new thread and let it run in the background. The task on the different thread is expected to carry out its functions independently of the main thread.

Let’s imagine that the following greetCustomer method is something we want to run on separate thread so that the main thread is not blocked:

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