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Nombre de programmes trouvés : 17833
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le (5m51s)

1.2. Introduction II - Coding Theory

In this session, we will give a brief introduction to Coding Theory. Claude Shannon's paper from 1948 entitled "A Mathematical Theory of Communication" gave birth to the disciplines of Information Theory and Coding Theory. The main goal of these disciplines is efficient transfer of reliable information. To be efficient, the transfer of information must not require a big amount of time and effort.  To be reliable, the transmitted and received data must resemble. However, during the transmission over a noisy channel, the information will be damaged. So, it has become necessary to develop ways of detecting when an error has ...
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le (4m15s)

1.3. Encoding (Linear Transformation)

In this session, we will talk about the easy map of the  - one-way trapdoor functions based on error-correcting codes. We suppose that the set of all messages that we wish to transmit is the set of k-tuples having elements from the field Fq. There are qk possible messages and we referred to it as the message space.  In order to detect and possibly correct errors, we add some redundancy, thus the k tuples will be embedded into n-tuples with n greater than k. In this MOOC, we will focus on linear ...
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le (4m47s)

1.4. Parity Checking

 There are two standard ways to describe a subspace, explicitly by giving a basis, or implicitly, by the solution space of the set of homogeneous linear equations. Therefore, there are two ways of describing a linear code, explicitly, as we have seen in the previous sequence, by a generator matrix, or implicitly, by the null space of a matrix. This is what we will see in this sequence. This leads to the following definition: H is a parity check matrix of a linear code, if the code is the null space ...
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le (8m26s)

1.5. Error Correcting Capacity

This sequence will be about the error-correcting capacity of a linear code. We describe the way of considering the space Fq^n as a metric space. This metric is necessary to justify the principle of decoding that is returning the nearest codeword to the received vector. The metric principle is based on the following concept: the Hamming distance between two vectors is the number of coordinates in which they differ. The Hamming weight of a vector is the number of non-zero coordinates. Here we give some examples. So, the Hamming distance ...
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le (8m14s)

1.6. Decoding (A Difficult Problem)

The process of correcting errors and obtaining back the message is called decoding. In this sequence, we will focus on this process, the decoding. We would like that the decoder of the received vector, which is the encoding of the original message plus a certain vector, is again the original message, for every message and every reasonable noisy pattern. The basis of decoding is the following principle, called Minimum Distance Decoding. Given a received vector, we look for a codeword that minimizes the Hamming distance with the received vector One of ...
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le (6m38s)

1.7. Reed-Solomon Codes

Reed-Solomon codes were introduced by Reed and Solomon in the 1960s. These codes are still used in storage device, from compact-disc player to deep-space application. And they are widely used mainly because of two features: first of all, because they are MDS code, that is, they attain the maximum error detection and correction capacity. The second thing is that they have efficient decoding algorithms. Reed-Solomon codes are particularly useful for burst error correction, that is, they are effective for channels that have memory.So, suppose that we consider n and k ...
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le (5m40s)

1.8. Goppa Codes

In this session, we will talk about another family of codes that have an efficient decoding algorithm: the Goppa codes. One limitation of the generalized Reed-Solomon codes is the fact that the length is bounded by the size of the field over which it is defined. This implies that these codes are useful when we use a large field size. In the sequence, we'll present a method to obtain a new code over small alphabets by exploiting the properties of the generalized Reed-Solomon codes. So, the idea is to construct a generalized ...
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le (5m35s)

1.9. McEliece Cryptosystem

This is the last session of the first week of this MOOC. We have already all the ingredients to talk about code-based cryptography. Recall that in 1976 Diffie and Hellman published their famous paper "New Directions in Cryptography", where they introduced public key cryptography providing a solution to the problem of key exchange. Mathematically speaking, public key cryptography considers the notion of one-way trapdoor function that is easy in one direction, hard in the reverse direction unless you have a special information called the trapdoor. The security of the most ...
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le (5m35s)

2.1. Formal Definition

Welcome to the second week of this MOOC entitled Code-Based Cryptography. This week, we will talk in detail about the McEliece cryptosystem. First, in this session, we will describe formally the McEliece and the Niederreiter systems, which are the principal public-key schemes, based on error-correcting code. Let K be a security parameter. An encryption scheme is defined by the following spaces: the space of all possible messages, the space of all ciphertexts, the space of the public-keys, and the space of the secret-keys.Then, we need to define the ...
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le (4m43s)

2.2. Security-Reduction Proof

Welcome to the second session. We will talk about the security-reduction proof. The security of a given cryptographic algorithm is reduced to the security of a known hard problem. To prove that a cryptosystem is secure, we select a problem which we know is hard to solve, and we reduce the problem to the security of the cryptosystem. Since the problem is hard to solve, the cryptosystem is hard to break. A security reduction is a proof that an adversary able to attack the scheme is able to solve some presumably hard ...
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