Symbolic Quantitative Information Flow for Probabilistic Programs
Abstract
It is of utmost importance to ensure that modern data intensive systems do not leak sensitive information. In this paper, the authors, who met thanks to Joost-Pieter Katoen, discuss symbolic methods to compute information-theoretic measures of leakage: entropy, conditional entropy, Kullback-Leibler divergence, and mutual information. We build on two semantic frameworks for symbolic execution of probabilistic programs. For discrete programs, we use weakest pre-expectation calculus to compute exact symbolic expressions for the leakage measures. Using Second Order Gaussian Approximation (SOGA), we handle programs that combine discrete and continuous distributions. However, in the SOGA setting, we approximate the exact semantics using Gaussian mixtures and compute bounds for the measures. We demonstrate the use of our methods in two widely used mechanisms to ensure differential privacy: randomized response and the Gaussian mechanism.
Keywords
Cite
@article{arxiv.2412.00907,
title = {Symbolic Quantitative Information Flow for Probabilistic Programs},
author = {Philipp Schröer and Francesca Randone and Raúl Pardo and Andrzej Wąsowski},
journal= {arXiv preprint arXiv:2412.00907},
year = {2024}
}
Comments
Pre-print of paper appearing in "In Principles of Verification: Cycling the Probabilistic Landscape-Essays Dedicated to Joost-Pieter Katoen on the Occasion of His 60th Birthday, 2024" (https://doi.org/10.1007/978-3-031-75783-9_6)