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20142026
most citedPrivacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences

154 citations · 897 across the 34 of their papers we have counts for

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Showing 2016Show all

5 papers · 1 filter

cs.PL2016★ 30 cited

Coupling proofs are probabilistic product programs

Gilles Barthe, Benjamin Grégoire, Justin Hsu +1

Couplings are a powerful mathematical tool for reasoning about pairs of probabilistic processes. Recent developments in formal verification identify a close connection between coup…

cs.LO2016★ 54 cited

Advanced Probabilistic Couplings for Differential Privacy

Gilles Barthe, Noémie Fong, Marco Gaboardi +3

Differential privacy is a promising formal approach to data privacy, which provides a quantitative bound on the privacy cost of an algorithm that operates on sensitive information.…

cs.PL2016★ 36 cited

Synthesizing Probabilistic Invariants via Doob's Decomposition

Gilles Barthe, Thomas Espitau, Luis María Ferrer Fioriti +1

When analyzing probabilistic computations, a powerful approach is to first find a martingale---an expression on the program variables whose expectation remains invariant---and then…

cs.LO2016

A program logic for union bounds

Gilles Barthe, Marco Gaboardi, Benjamin Grégoire +2

We propose a probabilistic Hoare logic aHL based on the union bound, a tool from basic probability theory. While the union bound is simple, it is an extremely common tool for analy…

cs.LO2016★ 98 cited

Proving Differential Privacy via Probabilistic Couplings

Gilles Barthe, Marco Gaboardi, Benjamin Grégoire +2

In this paper, we develop compositional methods for formally verifying differential privacy for algorithms whose analysis goes beyond the composition theorem. Our methods are based…