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Currently deployed public-key cryptosystems will be vulnerable to attacks by full-scale quantum computers. Consequently, "quantum resistant" cryptosystems are in high demand, and lattice-based cryptosystems, based on a hard problem known as Learning With Errors (LWE), have emerged as strong contenders for standardization. In this work, we train transformers to perform modular arithmetic and mix half-trained models and statistical cryptanalysis techniques to propose SALSA: a machine learning attack on LWE-based cryptographic schemes. SALSA can fully recover secrets for small-to-mid size LWE instances with sparse binary secrets, and may scale to attack real world LWE-based cryptosystems.
Author Information
Emily Wenger (University of Chicago)
Emily Wenger is a final year computer science PhD student at the University of Chicago, advised by Ben Zhao and Heather Zheng. Her research focuses on security and privacy issues of machine learning systems. Her work has been published at top computer security (CCS, USENIX, Oakland) and machine learning (NeurIPS, CVPR) conferences and has been covered by media outlets including the New York Times, MIT Tech Review, and Nature. She is the recipient of the GFSD, Harvey, and Neubauer fellowships. Previously, she worked for the US Department of Defense and interned at Meta AI Research.
Mingjie Chen (University of Birmingham)
Francois Charton (Meta AI)
Kristin E. Lauter (Facebook AI Research)
Kristin Estella Lauter is a mathematician and cryptographer who works at the interface of machine learning and cryptography. From 2008–2021, she was Partner Research Manager of the Cryptography and Privacy Research Group at Microsoft Research; her group developed SEAL, a leading OSS Homomorphic Encryption library. In April 2021, Lauter joined Facebook AI Research (FAIR) as the West Coast Director of Research Science. She was President of the Association for Women in Mathematics from 2015 - 2017. She is an elected Fellow of AAAS, AMS, SIAM, and AWM and an elected honorary member of the Royal Spanish Mathematical Society.
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