Bayesian Leakage Analysis
A Framework for Analyzing Leakage in Cryptography
Authors
Zachary Espiritu, Seny Kamara, Tarik Moataz
Zachary Espiritu
MongoDB, New York, United States
zachary dot espiritu at mongodb dot com
zachary dot espiritu at mongodb dot com
Seny Kamara
MongoDB, New York, United States
Brown University, Providence, United States
seny dot kamara at mongodb dot com
Brown University, Providence, United States
seny dot kamara at mongodb dot com
Tarik Moataz
MongoDB, New York, United States
tarik dot moataz at mongodb dot com
tarik dot moataz at mongodb dot com
Abstract
We introduce a framework based on Bayesian statistical inference for analyzing leakage in cryptography and its vulnerability to inference attacks. Our framework naturally integrates auxiliary information, defines a notion of adversarial advantage, and provides information-theoretic measures that capture the security of leakage patterns against both full and functional recovery attacks.
We present two main theorems that bound the advantage of powerful inference techniques: the maximum a posteriori (MAP), the maximum likelihood estimate (MLE) and the MAP test. Specifically, we show that the advantage of these methods is exponentially bounded by new entropy measures that capture the susceptibility of leakage patterns to inference.
To demonstrate the applicability of our framework, we design and implement an automated leakage attack engine, Bayle, which leverages a novel inference algorithm that efficiently computes MAP estimates for a large class of i.i.d. leakage models. These models include query equality leakage, the combination of query equality and volume leakage, and leakage patterns arising from naive conjunctions.
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History
Submitted: 2025-01-14
Accepted: 2025-03-11
Published: 2025-04-08
Accepted: 2025-03-11
Published: 2025-04-08
How to cite
Zachary Espiritu, Seny Kamara, and Tarik Moataz, Bayesian Leakage Analysis. IACR Communications in Cryptology, vol. 2, no. 1, Apr 08, 2025, doi: 10.62056/a36c0lmol.
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