Welcome to part 4 of my series on cryptography! After taking an in-depth look into ciphers in the previous two posts, now it’s time to delve into the other big category of cryptographic methods. So, this week’s post is going to be about cryptographic codes.
This post is part of my series Cryptography: Historical Intro & Combinatoric Analysis.
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In this week’s post, I’m going to introduce you to the world of polyalphabetic substitution ciphers. And to the most famous cipher from this category: the Vigenère cipher.
This post is part of my series Cryptography: Historical Intro & Combinatoric Analysis.
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Today, I’m going to introduce two ciphers from the dawn of cryptography: the column cipher and the Caesar cipher. These ciphers illustrate some of the first human ideas in the direction of cryptographic thought.
This post is part of my series Cryptography: Historical Intro & Combinatoric Analysis.
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Hi everyone, and welcome to my series on cryptography!
Initially, I started writing this as a single post, but as I was planning what I wanted to include, and seeing how long it would become, I decided it’s best to split it into separate posts.
Cryptography is the study of techniques and procedures, such as using codes and ciphers, for secure (secret) communication between two or more agents. More specifically, it deals with finding sophisticated ways of hiding the true content of messages exchanged between communicators. People use cryptography in military and diplomatic communication, password protection, securing online transactions, as well as in any other context where some secret information needs to be protected from being accessed or understood.
As you’ll see in future posts, probability theory too can play an important role in cryptography.
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In my previous two posts I sketched the frame of the big picture around probability distributions. In my introductory post I gave some intuition about the general concept and talked about the two major kinds: discrete and continuous distributions. And in the follow-up post I related the concepts of mean and variance to probability distributions. I showed that this connection itself goes through two fundamental concepts from probability theory: the law of large numbers and expected value.
Now I want to build on all these posts. My plan is to start introducing commonly used discrete and continuous distributions in separate posts dedicated to each one. And I want to start with the former, since they are significantly easier to understand.
The goal of the current post is to be a final warm-up before delving into the details of specific distributions.
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In my previous post I introduced you to probability distributions.
In short, a probability distribution is simply taking the whole probability mass of a random variable and distributing it across its possible outcomes. Since every random variable has a total probability mass equal to 1, this just means splitting the number 1 into parts and assigning each part to some element of the variable’s sample space (informally speaking).
In this post I want to dig a little deeper into probability distributions and explore some of their properties. Namely, I want to talk about the measures of central tendency (the mean) and dispersion (the variance) of a probability distribution.
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