By Rosario Gennaro, Matthew Robshaw

The 2 volume-set, LNCS 9215 and LNCS 9216, constitutes the refereed lawsuits of the thirty fifth Annual foreign Cryptology convention, CRYPTO 2015, held in Santa Barbara, CA, united states, in August 2015. The seventy four revised complete papers offered 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 buildings; multilinear maps and IO; pseudorandomness; block cipher cryptanalysis; integrity; assumptions; hash capabilities and circulate cipher cryptanalysis; implementations; multiparty computation; zero-knowledge; conception; signatures; non-signaling and information-theoretic crypto; attribute-based encryption; new primitives; and entirely homomorphic/functional encryption.

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Extra resources for Advances in Cryptology -- CRYPTO 2015: 35th Annual Cryptology Conference, Santa Barbara, CA, USA, August 16-20, 2015, Proceedings, Part I (Lecture Notes in Computer Science)

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E. ) AFRICACRYPT 2013. LNCS, vol. 7918, pp. 375–391. Springer, Heidelberg (2013) 42. : SVP challenge (2014). org/svp-challenge 43. : A hierarchy of polynomial time lattice basis reduction algorithms. Theoret. Comput. Sci. 53(2), 201–224 (1987) 44. : Lattice basis reduction: improved practical algorithms and solving subset sum problems. Math. Programming 66(2), 181–199 (1994) 45. : Improved Nguyen-Vidick heuristic sieve algorithm for shortest vector problem. In: ASIACCS, pp. 1–9 (2011) 46. : A three-level sieve algorithm for the shortest vector problem.

A third motivating reason is the fact that LWE-based cryptography is one of the areas where a quantum computer is not known to be able to break the primitives (contrary to factoring-based and discrete log-based primitives). This is sometimes referred to as being a tool in post-quantum cryptography. Let us state the LWE problem. Definition 1. Let n be a positive integer, q an odd prime, and let X be an error distribution selected as the discrete Gaussian distribution on Zq . Fix s to be a secret vector in Znq , chosen according to a uniform distribution.

LNCS, vol. 8282, pp. 29–47. se 2 Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China Abstract. In this paper we propose a new algorithm for solving the Learning With Errors (LWE) problem based on the steps of the famous Blum-Kalai-Wasserman (BKW) algorithm. The new idea is to introduce an additional procedure of mapping subvectors into codewords of a lattice code, thereby increasing the amount of positions that can be cancelled in each BKW step.

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