Neutrality-Based Symmetric Cryptanalysis

Cryptographic primitives are the basic components of any cryptographic tool. Block ciphers, stream ciphers and hash functions are the fundamental primitives of symmetric cryptography. In symmetric cryptography, the communicating parties perform essentially the same operation and use the same key, if any. This thesis concerns cryptanalysis of stream ciphers and hash functions. The main contribution of this work is introducing the concept of probabilistic neutrality for the arguments of a function, a generalization of the definition of neutrality. An input argument of a given function is called neutral if it does not affect the output of the function. This simple idea has already been implicitly used in key recovery cryptanalysis of block ciphers and stream ciphers. However, in 2004, Biham and Chen explicitly used the idea of neutrality to speed up collision finding algorithms for hash functions. We call an input argument of a function probabilistic neutral if it does not have a "significant" influence on the output of the function. Simply stated, it means that if the input argument is changed, the output of the function stays the same with a probability "close" to one. We will exploit the idea of probabilistic neutrality to assess the security of several stream ciphers and hash functions. Interestingly, all our cryptanalyses rely on neutrality and/or probabilistic neutrality. In other words, these concepts will appear as a common ingredient in all of our cryptanalytic algorithms. To the best of our knowledge, this is the first time that the probabilistic neutrality has found diverse applications in cryptanalysis.

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