Revisiting LFSRs for Cryptographic Applications

• 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
• ISSN: 0018-9448; 1557-9654
• DOI: 10.1109/TIT.2011.2164234

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.