MadS&P Seminar Guest Speaker - Avital Shafran

Abstract:
Given the recent progress in machine learning (ML), the cryptography community has started exploring the applicability of ML methods to the design of new cryptanalytic approaches. While current empirical results show promise, the extent to which such methods may outperform classical cryptanalytic approaches is still somewhat unclear. In this work, we initiate exploration of the theory of ML-based cryptanalytic techniques, in particular providing new results towards understanding whether they are fundamentally limited compared to traditional approaches. We introduce a unifying framework for capturing both ``sample-based’' and ``gradient-based’' adversaries. Within our framework, we establish a general feasibility result showing that any sample-based adversary can be simulated by a seemingly-weaker gradient-based one.Bio:
 

Bio:  Avital Shafran is a PhD student at the Hebrew University, advised by Prof. Shmuel Peleg and Prof. Gil Segev. Her research interests lie in the security of machine learning systems and the intersection of machine learning and cryptography.