The Discrete-Logarithm Problem with Preprocessing

Henry Corrigan-Gibbs and Dmitry Kogan

April 30-May 3, 2018, Tel Aviv, Israel

Best Young Researcher Paper Award


This paper studies discrete-log algorithms that use preprocessing. In our model, an adversary may use a very large amount of precomputation to produce an "advice" string about a specific group (e.g., NIST P-256). In a subsequent online phase, the adversary's task is to use the preprocessed advice to quickly compute discrete logarithms in the group. Motivated by surprising recent preprocessing attacks on the discrete-log problem, we study the power and limits of such algorithms.

In particular, we focus on generic algorithms—these are algorithms that operate in every cyclic group. We show that any generic discrete-log algorithm with preprocessing that uses an S-bit advice string, runs in online time T, and succeeds with probability ε in a group of prime order N must satisfy ST2=Ω(εN). Our lower bound, which is tight up to logarithmic factors, uses a synthesis of incompressibility techniques and classic methods for generic-group lower bounds. We apply our techniques to prove related lower bounds for the CDH, DDH, and multiple-discrete-log problems.

Finally, we demonstrate two new generic preprocessing attacks: one for the multiple-discrete-log problem and one for certain decisional-type problems in groups. This latter result demonstrates that, for generic algorithms with preprocessing, distinguishing tuples of the form (g,gx,g(x2)) from random is much easier than the discrete-log problem.