An Optimal Distributed Discrete Log Protocol with Applications to Homomorphic Secret Sharing

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The distributed discrete logarithm (DDL) problem was introduced by Boyle, Gilboa and Ishai at CRYPTO 2016. A protocol solving this problem was the main tool used in the share conversion procedure of their homomorphic secret sharing (HSS) scheme which allows non-interactive evaluation of branching programs among two parties over shares of secret inputs. Let g be a generator of a multiplicative group G. Given a random group element gx and an unknown integer b∈ [- M, M] for a small M, two parties A and B (that cannot communicate) successfully solve DDL if A(gx) - B(gx+b) = b. Otherwise, the parties err. In the DDL protocol of Boyle et al., A and B run in time T and have error probability that is roughly linear in M / T. Since it has a significant impact on the HSS scheme’s performance, a major open problem raised by Boyle et al. was to reduce the error probability as a function of T. In this paper we devise a new DDL protocol that substantially reduces the error probability to O(M· T- 2). Our new protocol improves the asymptotic evaluation time complexity of the HSS scheme by Boyle et al. on branching programs of size S from O(S2) to O(S3 / 2). We further show that our protocol is optimal up to a constant factor for all relevant cryptographic group families, unless one can solve the discrete logarithm problem in a short interval of length R in time o(R). Our DDL protocol is based on a new type of random walk that is composed of several iterations in which the expected step length gradually increases. We believe that this random walk is of independent interest and will find additional applications.

Original languageAmerican English
Pages (from-to)824-873
Number of pages50
JournalJournal of Cryptology
Issue number3
StatePublished - 1 Jul 2020


  • Discrete logarithm
  • Discrete logarithm in a short interval
  • Fully homomorphic encryption
  • Homomorphic secret sharing
  • Random walk
  • Share conversion

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Applied Mathematics


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