MiMC: Efficient encryption and cryptographic hashing with minimal multiplicative complexity

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Abstract

We explore cryptographic primitives with low multiplicative complexity. This is motivated by recent progress in practical applications of secure multi-party computation (MPC), fully homomorphic encryption (FHE), and zero-knowledge proofs (ZK) where primitives from symmetric cryptography are needed and where linear computations are, compared to non-linear operations, essentially “free”. Starting with the cipher design strategy “LowMC” from Eurocrypt 2015, a number of bitoriented proposals have been put forward, focusing on applications where the multiplicative depth of the circuit describing the cipher is the most important optimization goal. Surprisingly, albeit many MPC/FHE/ZK-protocols natively support operations in GF(p) for large p, very few primitives, even considering all of symmetric cryptography, natively work in such fields. To that end, our proposal for both block ciphers and cryptographic hash functions is to reconsider and simplify the round function of the Knudsen-Nyberg cipher from 1995. The mapping F(x) := x3 is used as the main component there and is also the main component of our family of proposals called “MiMC”. We study various attack vectors for this construction and give a new attack vector that outperforms others in relevant settings. Due to its very low number of multiplications, the design lends itself well to a large class of applications, especially when the depth does not matter but the total number of multiplications in the circuit dominates all aspects of the implementation. With a number of rounds which we deem secure based on our security analysis, we report on significant performance improvements in a representative use-case involving SNARKs.

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APA

Albrecht, M., Grassi, L., Rechberger, C., Roy, A., & Tiessen, T. (2016). MiMC: Efficient encryption and cryptographic hashing with minimal multiplicative complexity. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10031 LNCS, pp. 191–219). Springer Verlag. https://doi.org/10.1007/978-3-662-53887-6_7

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