Revisiting LFSRs for Cryptographic Applications

Linear finite state machines (LFSMs) are particular primitives widely used in information theory, coding theory and cryptography. Among those linear automata, a particular case of study is linear feedback shift registers (LFSRs) used in many cryptographic applications such as design of stream cipher...

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Bibliographic Details
Published in:IEEE transactions on information theory 2011-12, Vol.57 (12), p.8095-8113
Main Authors: Arnault, F., Berger, T., Minier, M., Pousse, B.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Automata
Coding theory
Coding, codes
Criteria
Cryptography
Exact sciences and technology
Finite state machines
Information theory
Information, signal and communications theory
linear feedback shift registers (LFSRs)
linear finite state machines (LFSMs)
m -sequences
Mathematical analysis
Matrices
Matrix methods
Polynomials
Representations
Signal and communications theory
Sparsity
Telecommunications and information theory
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Summary:Linear finite state machines (LFSMs) are particular primitives widely used in information theory, coding theory and cryptography. Among those linear automata, a particular case of study is linear feedback shift registers (LFSRs) used in many cryptographic applications such as design of stream ciphers or pseudo-random generation. LFSRs could be seen as particular LFSMs without inputs. In this paper, we first recall the description of LFSMs using traditional matrices representation. Then, we introduce a new matrices representation with polynomial fractional coefficients. This new representation leads to sparse representations and implementations. As direct applications, we focus our work on the Windmill generators case, used for example in the E0 stream cipher and on other general applications that use this new representation. In a second part, a new design criterion called diffusion delay for LFSRs is introduced and well compared with existing related notions. This criterion represents the diffusion capacity of an LFSR. Thus, using the matrices representation, we present a new algorithm to randomly pick LFSRs with good properties (including the new one) and sparse descriptions dedicated to hardware and software designs. We present some examples of LFSRs generated using our algorithm to show the relevance of our approach.
ISSN:0018-9448
1557-9654
DOI:10.1109/TIT.2011.2164234