Download Advances in Cryptology -- CRYPTO 2015: 35th Annual by Rosario Gennaro, Matthew Robshaw PDF
By Rosario Gennaro, Matthew Robshaw
The volume-set, LNCS 9215 and LNCS 9216, constitutes the refereed lawsuits of the thirty fifth Annual overseas Cryptology convention, CRYPTO 2015, held in Santa Barbara, CA, united states, in August 2015. The seventy four revised complete papers provided have been conscientiously reviewed and chosen from 266 submissions. The papers are prepared within the following topical sections: lattice-based cryptography; cryptanalytic insights; modes and structures; multilinear maps and IO; pseudorandomness; block cipher cryptanalysis; integrity; assumptions; hash capabilities and movement cipher cryptanalysis; implementations; multiparty computation; zero-knowledge; conception; signatures; non-signaling and information-theoretic crypto; attribute-based encryption; new primitives; and completely homomorphic/functional encryption.
Read Online or Download Advances in Cryptology -- CRYPTO 2015: 35th Annual Cryptology Conference, Santa Barbara, CA, USA, August 16-20, 2015, Proceedings, Part I PDF
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- Advances in Cryptology -- CRYPTO 2015: 35th Annual Cryptology Conference, Santa Barbara, CA, USA, August 16-20, 2015, Proceedings, Part II
Additional resources for Advances in Cryptology -- CRYPTO 2015: 35th Annual Cryptology Conference, Santa Barbara, CA, USA, August 16-20, 2015, Proceedings, Part I
Another variant is the so-called decision LWE problem. In this case the problem is to distinguish samples drawn from Ls,X and samples drawn from a uniform distribution on Znq × Zq . Typically, we are then interested in distinguishers with non-negligible advantage. The parameters of an LWE instance are typically chosen with some internal relations. The prime q is chosen as a polynomial in n, and the discrete Gaussian distribution X has mean zero and standard deviation σ = α · q for some small 2 α.
08n+o(n) . 21n+o(n) short vectors for the next iteration. In this case however we never stored all hash tables in memory at the same time, and the memory increase compared to the NV-sieve is asymptotically negligible. This leads to the following result. Theorem 2. 2075n+o(n) . These complexities are indicated by the left-most blue point in Fig. 1. Note that this choice of parameters balances the costs of computing hashes and comparing vectors; the fact that the blue point in Fig. 1 does not lie on the “Time = Space”-line does not mean we can further reduce the time complexity.
The purpose of our experiments is only to obtain a fair comparison of the two algorithms and to try to estimate and compare the asymptotic behaviors of these algorithms. Details on a more optimized implementation of the HashSieve are given in . Computations. Figure 3b shows the number of inner products computed by the HashSieve for comparing vectors and for computing hashes. We have chosen 18 T. 24 ry n+7 2 mem 2 o mem GaussSieve HashSieve HS with probing 50 60 70 80 Dimension n (e) Space complexities Fig.