Parallel versions of collision search algorithms require a significant amount of memory to store a proportion of the points computed by the pseudo-random walks. Implementations available in the literature use a hash table to store these points and allow fast memory access. We provide theoretical evidence that memory is an important factor in determ...
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... Unlike other attacks in this thesis, the pcs does not use a sat solver. Most of the contributions of this work are presented in [ TID17 ] . ...
In this thesis, we explore the use of combinatorial techniques, such as graph-based algorithms and constraint satisfaction, in cryptanalysis. Our main focus is on the elliptic curve discrete logarithm problem. First, we tackle this problem in the case of elliptic curves defined over prime-degree binary extension fields, using the index calculus attack. A crucial step of this attack is solving the point decomposition problem, which consists in finding zeros of Semaev’s summation polynomials and can be reduced to the problem of solving a multivariate Boolean polynomial system. To this end, we encode the point decomposition problem as a logical formula and define it as an instance of the SAT problem. Then, we propose an original XOR-reasoning SAT solver, named WDSat, dedicated to this specific problem. As Semaev’s polynomials are symmetric, we extend the WDSat solver by adding a novel symmetry breaking technique that, in contrast to other symmetry breaking techniques, is not applied to the modelization or the choice of a factor base, but to the solving process. Experimental running times show that our SAT-based solving approach is significantly faster than current algebraic methods based on Gröbner basis computation. In addition, our solver outperforms other state-of-the-art SAT solvers, for this specific problem. Finally, we study the elliptic curve discrete logarithm problem in the general case. More specifically, we propose a new data structure for the Parallel Collision Search attack proposed by van Oorschot and Wiener, which has significant consequences on the memory and time complexity of this algorithm.
The security guarantees of most isogeny-based protocols rely on the computational hardness of finding an isogeny between two supersingular isogenous curves defined over a prime field Fq with q a power of a large prime p. In most scenarios, the isogeny is known to be of degree ℓe for some small prime ℓ. We call this problem the Supersingular Fixed-Degree Isogeny Path (SIPFD) problem. It is believed that the most general version of SIPFD is not solvable faster than in exponential time by classical as well as quantum attackers. In a classical setting, a meet-in-the-middle algorithm is the fastest known strategy for solving the SIPFD. However, due to its stringent memory requirements, it quickly becomes infeasible for moderately large SIPFD instances. In a practical setting, one has therefore to resort to time-memory trade-offs to instantiate attacks on the SIPFD. This is particularly true for GPU platforms, which are inherently more memory-constrained than CPU architectures. In such a setting, a van Oorschot-Wiener-based collision finding algorithm offers a better asymptotic scaling. Finding the best algorithmic choice for solving instances under a fixed prime size, memory budget and computational platform remains so far an open problem. To answer this question, we present a precise estimation of the costs of both strategies considering most recent algorithmic improvements. As a second main contribution, we substantiate our estimations via optimized software implementations of both algorithms. In this context, we provide the first optimized GPU implementation of the van Oorschot-Wiener approach for solving the SIPFD. Based on practical measurements we extrapolate the running times for solving different-sized instances. Finally, we give estimates of the costs of computing a degree-288 isogeny using our CUDA software library running on an NVIDIA A100 GPU server.