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    <title>Finiasz, Matthieu (1977-...)</title>
    <description><![CDATA[ Auteur d'une thèse en mathématiques et informatique à l'Ecole polytechnique de Paris

 ]]></description>
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        <title>Finiasz, Matthieu (1977-...)</title>
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      <itunes:name>Finiasz, Matthieu (1977-...)</itunes:name>
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          <item>
        <title>1.1. Introduction I - Cryptography</title>
        <link>https://www.canal-u.tv/92639</link>
                  <description><![CDATA[ Welcome to this MOOC which is entitled: code-based cryptography. This MOOC is divided in five weeks. The first week, we will talk about error-correcting codes and cryptography, this is an introduction week.Then, we will introduce the McEliece cryptosystem, and the security proof for the McEliece cryptosystem. We will talk about generic attack, message attack, in other words. This course will be given by Nicolas Sendrier. Then, we'll give structural attacks or key attacks, and at the end, we will ]]></description>
                          <pubDate>Fri, 17 Feb 2017 12:00:00 +0100</pubDate>
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        <title>1.2. Introduction II - Coding Theory</title>
        <link>https://www.canal-u.tv/92675</link>
                  <description><![CDATA[ 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 o ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>1.3. Encoding (Linear Transformation)</title>
        <link>https://www.canal-u.tv/92679</link>
                  <description><![CDATA[ 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  ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>1.4. Parity Checking</title>
        <link>https://www.canal-u.tv/92681</link>
                  <description><![CDATA[  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 th ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>1.5. Error Correcting Capacity</title>
        <link>https://www.canal-u.tv/92683</link>
                  <description><![CDATA[ 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. Her ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>1.6. Decoding (A Difficult Problem)</title>
        <link>https://www.canal-u.tv/92685</link>
                  <description><![CDATA[ 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 ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>1.7. Reed-Solomon Codes</title>
        <link>https://www.canal-u.tv/92687</link>
                  <description><![CDATA[ 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 a ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>1.8. Goppa Codes</title>
        <link>https://www.canal-u.tv/92691</link>
                  <description><![CDATA[ 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 ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>1.9. McEliece Cryptosystem</title>
        <link>https://www.canal-u.tv/92693</link>
                  <description><![CDATA[ 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 ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>2.1. Formal Definition</title>
        <link>https://www.canal-u.tv/92701</link>
                  <description><![CDATA[ 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-ke ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>2.2. Security-Reduction Proof</title>
        <link>https://www.canal-u.tv/92703</link>
                  <description><![CDATA[ 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 ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>2.3. McEliece Assumptions</title>
        <link>https://www.canal-u.tv/92711</link>
                  <description><![CDATA[ In this session, we will  talk about McEliece assumptions. The security of the  McEliece scheme is based on two assumptions as we have  already seen: the hardness of decoding a random linear  code and the problem of distinguishing a code with a  prescribed structure from a random one. In this sequence, we will study  in detail these two assumptions. The first assumption claims that  decoding a random linear code is difficult.  First, notice that the  general decoding problem is basically a re-wr ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>2.4. Notions of Security</title>
        <link>https://www.canal-u.tv/92713</link>
                  <description><![CDATA[ In this session, we will study the  notion of security of public-key scheme. A public-key scheme is  one-way if the probability of success of any adversary  running in polynomial time is negligible. That is,  without the private key, it is computationally impossible  to recover the plaintext. For the McEliece, if we  assume that the general decoding problem of a linear code  is on average a difficult problem and there exists no  efficient distinguisher for Goppa codes, then the McEliece  scheme  ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>2.5. Critical Attacks - Semantic Secure Conversions</title>
        <link>https://www.canal-u.tv/92715</link>
                  <description><![CDATA[ In this session, we will  study critical attacks against the public-key cryptosystem.  The partial knowledge on the plaintext reduces  drastically the computational cost of the attack to the  McEliece cryptosystem. For example, suppose that the  adversary knows r bits of the plaintext. Then the difficulty of  recovering the remaining k - r   bits in the complete  McEliece with parameters [n, k] is  equivalent to that of  recovering the full plaintext in   the McEliece with  parameters [n, k - r] ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>2.6. Reducing the Key Size</title>
        <link>https://www.canal-u.tv/92725</link>
                  <description><![CDATA[ In the next three sessions,  I will explain how to reduce the key size of  code-based cryptosystem. Circulant matrices are the  central point in many attempts to reduce the key size of  code-based cryptosystems since they provide  efficient representation. A circulant matrix is  a square matrix, its rows are obtained by  cyclically shifting the first row. An alternative representation of an  n-tuple of elements is using polynomial. Thus, this matrix can be  described by a polynomial. And the i-t ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>2.7. Reducing the Key Size - LDPC codes</title>
        <link>https://www.canal-u.tv/92727</link>
                  <description><![CDATA[ LDPC codes have an  interesting feature: they are free of algebraic structure. We will study in detail  this proposal for the McEliece cryptosystem in this  session. LDPC codes were originally introduced by Gallager, in  his doctoral thesis, in 1963. One of the characteristic of  LDPC codes is the existence of several iterative  decoding algorithms which achieve excellent performances.  Tanner, later, in the 1981, introduced a graphical  representation to these codes as bipartite graph. However, ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>2.8. Reducing the Key Size - MDPC codes</title>
        <link>https://www.canal-u.tv/92729</link>
                  <description><![CDATA[ This is the last session where we  will talk about reducing the key size. Here we will  introduce the MDPC codes. In 2012, the MDPC codes were  proposed for the McEliece schemes. An MDPC code is a  code that admits a binary   moderate density-parity check matrix. Typically, the Hamming  weight of each row is of the order the square of the length. In this sequence, I will  describe this scheme of quasi-cyclic MDPC McEliece for  a binary code of rate one half. So, we use circulant  matrices of blo ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>2.9. Implementation</title>
        <link>https://www.canal-u.tv/92731</link>
                  <description><![CDATA[ This is the last  session of the second week. The cryptography community  has different options for using public key cryptosystems,  among others, they have RSA or DSA. But … McEliece has the same level  of performance of the current protocol? eBATS is a competition to  identify the most efficient public key cryptosystem.  They mesure among other criteria: the key size, the  time of the key generation algorithm, the encryption  algorithm, and the decryption algorithm. The eBATS benchmarking incl ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>3.1. From Generic Decoding to Syndrome Decoding</title>
        <link>https://www.canal-u.tv/92733</link>
                  <description><![CDATA[ Welcome to the third week of the MOOC on  code-based cryptography. This week, we will  learn about message attacks. Among the ten sessions of  this week, the first six will present the most essential  part of generic decoding and the last four will be  devoted to more advanced matters. The first session is  about generic decoding; a   presentation of what a message  attack and what generic decoding is about. A cryptogram in the McEliece  encryption scheme has the following form. A cryptogram is  ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>3.2. Combinatorial Solutions: Exhaustive Search and Birthday Decoding</title>
        <link>https://www.canal-u.tv/92743</link>
                  <description><![CDATA[ In this session, I will  detail two combinatorial solutions to the decoding problem. The first one is  the Exhaustive Search. To find our w columns,  we will simply enumerate all the tuples j1 to  jw and check whether the corresponding column  plus the syndrome is equal to zero modulo 2. In detail  here is how we will do. We have w loops enumerating the indices from j1 to jw, and in the innermost loop,  we add the w columns plus the syndrome and  either we test the value of the syndrome or we st ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>3.3. Information Set Decoding: the Power of Linear Algebra</title>
        <link>https://www.canal-u.tv/92745</link>
                  <description><![CDATA[ In this third session, we  will present the most important concept of the week:  Information Set Decoding. The problem of decoding is not  only a combinatorial problem. Because we are dealing with linear  code, we may also use Linear Algebra. In particular, we are able  to transform the Computational Syndrome Decoding  problem by multiplying the matrix by a  permutation P on the right and a nonsingular matrix U on the left. This will transform the  problem of syndrome decoding into an equivalent ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>3.4. Complexity Analysis</title>
        <link>https://www.canal-u.tv/92753</link>
                  <description><![CDATA[ In this session, I will  present the main technique to make the analysis of the  various algorithms presented in this course. So, Information Set  Decoding refers to a family of algorithms which is  similar to the Prange algorithm that we have just seen. All variants of Information  Set Decoding repeat a large number of independent  iterations which all have a constant cost K and a success probability P. This means that this  iteration has to be repeated an expected number of  times N where N =  ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>3.5. Lee and Brickell Algorithm</title>
        <link>https://www.canal-u.tv/92755</link>
                  <description><![CDATA[ In this fifth session,  we will study a variant of information set decoding  proposed by Lee and Brickell. So, the main idea  consists in relaxing the Prange algorithm to amortize the  cost of the Gaussian elimination. So, instead of error patterns with all  positions on the left, we will allow error patterns of  the form given in the slide. So, in the left part we  have w-p coordinate to 1 and on the right hand side  we allow a small number p of positions to have a value 1. So, at each iteratio ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>3.6. Stern/Dumer Algorithm</title>
        <link>https://www.canal-u.tv/92761</link>
                  <description><![CDATA[ In this session, we will  present the Stern algorithm for decoding. In fact, the idea is to combine two algorithms  that we have seen before, the Lee and Brickell algorithm  and the Birthday Decoding.  So, instead of a full  Gaussian elimination, we will simply have a partial Gaussian  elimination as presented here. And if we look at the lower  part, what is called step 1, in red here in this  slide, it is, in fact, a smaller CSD problem with  a smaller matrix H', with a smaller target syndrome  ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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        <title>3.7. May, Meurer, and Thomae Algorithm</title>
        <link>https://www.canal-u.tv/92763</link>
                  <description><![CDATA[ So, with the session  7 we are entering the most advanced part of that course. The idea of what I called the  Improved Birthday  Decoding is to use the so-called "representation technique"  introduced by Howgrave-Graham and Joux in 2010 in which we will relax the way  we construct the two lists in Birthday Decoding. So, if you  remember, we could relax the size of the  matrices H1 and H2 slightly to gain a polynomial  factor on Birthday Decoding. But, we may push the idea  further and increase t ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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                      </item>
          <item>
        <title>3.8. Becker, Joux, May, and Meurer Algorithm</title>
        <link>https://www.canal-u.tv/92765</link>
                  <description><![CDATA[ Now in session 8, we will  present yet another evolution of information set decoding. Before presenting this  improvement, we will first improve the Birthday Decoding  algorithm what I call a Further Improvement of Birthday Decoding. I will consider the  two following lists. The difference between  those two lists and those we had before is the + ɛ that you  can find in the weight of the errors e1 and e2. Those lists depend on  another parameter ɛ. What is the meaning  of that parameter? Well, t ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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                      </item>
          <item>
        <title>3.9. Generalized Birthday Algorithm for Decoding</title>
        <link>https://www.canal-u.tv/92767</link>
                  <description><![CDATA[ The session nine is  devoted to the application of the Generalized Birthday  Algorithm to decoding. The Generalized Birthday  Algorithm was presented by David Wagner in 2002, in a  more general context. In fact, at order a,  the Generalized Birthday Algorithm solves the  following problem: we are given 2^a lists of vectors of size L and we want to find xi, one in every list Li,  such that the sum of all the xi is 0. If the lists Li are large enough,  then the algorithm runs in time 2^(l/(a+1)).  ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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                      </item>
          <item>
        <title>3.10. Decoding One Out of Many</title>
        <link>https://www.canal-u.tv/92769</link>
                  <description><![CDATA[ The final session of this week is  devoted to Decoding One Out of Many. Decoding One Out of Many  is interested in solving the following variant  of Syndrome Decoding. In this variant, the only  difference with the usual Syndrome Decoding is  that we are interested in a set of syndromes  rather than a single syndrome. So, the instance will be S, a set of syndromes of size N. H, a parity-check  matrix and w an integer, the weight we are looking for. And we are interested  in an error e, such that ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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                      </item>
          <item>
        <title>4.1. Introduction</title>
        <link>https://www.canal-u.tv/92775</link>
                  <description><![CDATA[ Welcome to the fourth week of the MOOC Code-based Cryptography. Recall that we have mainly  two ways of cryptanalyzing in the McEliece cryptosystem.  We have Message Attacks, which address the problem of  decoding a random linear code; these attacks has already  been studied in the third week, by Nicolas Sendrier. Notice that efficient  generic attack just makes the use of larger code in the  McEliece scheme necessary. And we also have Key Attacks. These attacks try to  retrieve the code structu ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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                      </item>
          <item>
        <title>4.2. Support Splitting Algorithm</title>
        <link>https://www.canal-u.tv/92777</link>
                  <description><![CDATA[ This session will be about  the support splitting algorithm. For the q-ary case, there are  three different notions of equivalence. The general one: two codes  of length n are semi-linear equivalent if they are  equal up to a fixed linear map. Each linear map is the  composition of a permutation, a scalar multiplication, which  could vary for each coordinate, and a field automorphism.  But for this session, we consider a more restrictive  definition, which coincides with the general case  for bi ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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                      </item>
          <item>
        <title>4.3. Distinguisher for GRS codes</title>
        <link>https://www.canal-u.tv/92779</link>
                  <description><![CDATA[ In this session we will see  that generalized Reed-Solomon codes behave differently than random  codes with respect to the star operation. Thus we can define a distinguisher  for Generalized Reed-Solomon codes. Let us recall the definition of  Generalized Reed-Solomon codes. We will need an n-tuple of  mutually distinct elements of Fq. We need a vector b which is  an n-tuple of nonzero elements of Fq. We need  to define the vector space of all polynomials of degree  at most k and we also need to ]]></description>
                          <pubDate>Mon, 20 Feb 2017 12:00:00 +0100</pubDate>
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                      </item>
          <item>
        <title>4.4. Attack against subcodes of GRS codes</title>
        <link>https://www.canal-u.tv/92783</link>
                  <description><![CDATA[ In this session, we will  talk about using subcodes of a Generalized Reed–Solomon code  for the McEliece Cryptosystem. Recall that to avoid the  attack of Sidelnikov and Shestakov, Berger and  Loidreau proposed to replace Generalized Reed–Solomon  codes by some random subcodes of small codimension.  However, this attack has been broken by Wieschebrink in 2006  using square code considerations. The idea of the  attack is very simple.   The public key is a subcode  of large dimension, otherwise a  ]]></description>
                          <pubDate>Tue, 21 Feb 2017 12:00:00 +0100</pubDate>
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                      </item>
          <item>
        <title>4.5. Error-Correcting Pairs</title>
        <link>https://www.canal-u.tv/92785</link>
                  <description><![CDATA[ We present in this session a  general decoding method for linear codes. And we will see it in an example. Let C be a generalized  Reed-Solomon code of dimension k associated to the pair (c, d). Then, its dual is again a  generalized Reed-Solomon code with the same locator  and another column multiplier we will denote by d^ (d dual). Now, consider the codes A and B.  These codes have not  been chosen at random. First, notice that the star product  of these two codes is the dual of C. Suppose that ]]></description>
                          <pubDate>Tue, 21 Feb 2017 12:00:00 +0100</pubDate>
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                      </item>
          <item>
        <title>4.6. Attack against GRS codes</title>
        <link>https://www.canal-u.tv/92787</link>
                  <description><![CDATA[ In this session we will  discuss the proposal of using generalized Reed-Solomon codes  for the McEliece cryptosystem. As we have already said,  generalized Reed-Solomon codes were proposed in  1986 by Niederreiter. Recall that these codes are  MDS, that is, they attain the maximum error correcting  capacity which is interpreted as shorter keys for the  same level of security. Moreover, these codes have  efficient decoding algorithms so they are suitable  candidates for code-based cryptography. B ]]></description>
                          <pubDate>Tue, 21 Feb 2017 12:00:00 +0100</pubDate>
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                      </item>
          <item>
        <title>4.7. Attack against Reed-Muller codes</title>
        <link>https://www.canal-u.tv/92789</link>
                  <description><![CDATA[ In this session, we will  introduce an attack against binary Reed-Muller codes. Reed-Muller codes were  introduced by Muller in 1954 and, later, Reed provided the  first efficient decoding algorithm for these codes. Reed-Muller are just a  generalization of generalized Reed-Solomon codes. Generalized Reed-Solomon  codes are evaluation of univariate polynomials,  and Reed-Muller codes are evaluation of  multivariate polynomials. We will study binary Reed-Muller codes. The binary Reed-Muller  cons ]]></description>
                          <pubDate>Tue, 21 Feb 2017 12:00:00 +0100</pubDate>
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                      </item>
          <item>
        <title>4.8. Attack against Algebraic Geometry codes</title>
        <link>https://www.canal-u.tv/92791</link>
                  <description><![CDATA[ In this session, we will present an  attack against Algebraic Geometry codes (AG codes). Algebraic Geometry codes  is determined by a triple. First of all, an  algebraic curve of genus g, then a n-tuple of rational points  and then a divisor which has disjoint support from the n-tuple P. Then, the Algebraic  Geometry code is obtained by evaluating at P all  functions that belong to the vector space associated to the divisor E. Some properties of these  codes are nearly optimal codes, that is, th ]]></description>
                          <pubDate>Tue, 21 Feb 2017 12:00:00 +0100</pubDate>
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          <item>
        <title>4.9. Goppa codes still resist</title>
        <link>https://www.canal-u.tv/92793</link>
                  <description><![CDATA[ All the results that we   have seen this week doesn't mean that code based  cryptography is broken. So in this session we will  see that Goppa code still resists to all these attacks. So recall that it is  assumed that Goppa codes are pseudorandom, that is  there exist no efficient distinguisher for Goppa  code. An efficient distinguisher was built for the case of high rate codes, where the rate  is very close to 1, but no generalization of this  distinguisher is known. The best known attacks ar ]]></description>
                          <pubDate>Tue, 21 Feb 2017 12:00:00 +0100</pubDate>
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          <item>
        <title>5.1. Code-Based Digital Signatures</title>
        <link>https://www.canal-u.tv/92797</link>
                  <description><![CDATA[ Welcome to the last  week of this MOOC on code-based cryptography. This week, we will be  discussing other cryptographic constructions  relying on coding theory. We have seen how to do  public key encryption and now we will see other kind of constructions. This first sequence we'll  be having a look at digital signatures and how to design  code-based digital signatures. So, what is digital signature? A digital signature is meant  to replace a paper signature on a digital document.  Exactly like  ]]></description>
                          <pubDate>Tue, 21 Feb 2017 12:00:00 +0100</pubDate>
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          <item>
        <title>5.2. The Courtois-Finiasz-Sendrier (CFS) Construction</title>
        <link>https://www.canal-u.tv/92799</link>
                  <description><![CDATA[ In this session, I am  going to present the Courtois-Finiasz-Sendrier  Construction of a code-based digital signature. In the previous session,  we have seen that it is impossible to hash a  document into decodable syndromes. But it is possible to hash  onto the space of all syndromes. The document is not always decodable. And we are going to see two  techniques to work around this problem. The first technique is to  add a counter to the document. This way, we hash both the  counter and the docu ]]></description>
                          <pubDate>Tue, 21 Feb 2017 12:00:00 +0100</pubDate>
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                      </item>
          <item>
        <title>5.3. Attacks against the CFS Scheme</title>
        <link>https://www.canal-u.tv/92805</link>
                  <description><![CDATA[ In this session, we will  have a look at the attacks against the CFS signature scheme. As for public-key  encryption, there are two kinds of attacks against signature schemes. First kind of attack is  key recovery attacks where an attacker tries to  recover the secret key from the knowledge of the public  key. These attacks are exactly the same as against the McEliece  cryptosystem that you have seen last week. The only difference is  the parameters. Here in the signature, we have a  small t and ]]></description>
                          <pubDate>Tue, 21 Feb 2017 12:00:00 +0100</pubDate>
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                      </item>
          <item>
        <title>5.4. Parallel-CFS</title>
        <link>https://www.canal-u.tv/92807</link>
                  <description><![CDATA[ In this session, I will  present a variant of the CFS signature scheme called  parallel-CFS. We start from a simple question: what  happens if you try to use two different hash functions and  compute two different CFS signatures? For the signer, you simply  take twice as much computation because you have to  do two signatures. And then, the signature is  twice longer because you have just to concatenate two signatures. One would assume that for  the attacker it is the same, he simply has to forg ]]></description>
                          <pubDate>Tue, 21 Feb 2017 12:00:00 +0100</pubDate>
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          <item>
        <title>5.5. Stern’s Zero-Knowledge Identification Scheme</title>
        <link>https://www.canal-u.tv/92809</link>
                  <description><![CDATA[ In this session, we are  going to have a look at Stern’s Zero-Knowledge Identification Scheme. So, what is a  Zero-Knowledge Identification Scheme? An identification scheme  allows a prover to prove his identity to a verifier. And the Zero-Knowledge  Protocol is an interactive protocol where one proves  the knowledge of something, without revealing any information  on this knowledge, on this element. So, Stern’s Identification  Scheme was invented in 1993 and security relies on the  syndrome dec ]]></description>
                          <pubDate>Tue, 21 Feb 2017 12:00:00 +0100</pubDate>
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          <item>
        <title>5.6. An Efficient Provably Secure One-Way Function</title>
        <link>https://www.canal-u.tv/92811</link>
                  <description><![CDATA[ In this session, we are  going to see how to build an efficient provably secure  one-way function from coding theory. As you know, a one-way  function is a function which is simple to evaluate and  which should be as fast as possible and hard to invert,  ideally with good security arguments. There are many  applications of one-way functions, especially in symmetric  cryptography. For example, for compression functions to  build hash functions, expansion functions to build pseudorandom  number ge ]]></description>
                          <pubDate>Tue, 21 Feb 2017 12:00:00 +0100</pubDate>
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          <item>
        <title>5.7. The Fast Syndrome-Based (FSB) Hash Function</title>
        <link>https://www.canal-u.tv/92813</link>
                  <description><![CDATA[ In the last session of this  week, we will have a look at the FSB Hash Function which  is built using the one-way function we saw in  the previous session. What are the requirements for  a cryptographic hash function? So, it is a function which  takes an input of arbitrary size and outputs a fixed size. From a security point of  view, it should be hard to solve any of the three following  problems: first, find an input with a given hash which is  what we call preimage attacks; find an input with ]]></description>
                          <pubDate>Tue, 21 Feb 2017 12:00:00 +0100</pubDate>
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