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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Beyond Differential Privacy: Composition Theorems and Relational Logic for f-divergences between Probabilistic Programs
verfasst von : Gilles Barthe, Federico Olmedo
Erschienen in: Automata, Languages, and Programming
Verlag: Springer Berlin Heidelberg
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f
-divergences form a class of measures of distance between probability distributions; they are widely used in areas such as information theory and signal processing. In this paper, we unveil a new connection between
f
-divergences and differential privacy, a confidentiality policy that provides strong privacy guarantees for private data-mining; specifically, we observe that the notion of
α
-distance used to characterize approximate differential privacy is an instance of the family of
f
-divergences. Building on this observation, we generalize to arbitrary
f
-divergences the sequential composition theorem of differential privacy. Then, we propose a relational program logic to prove upper bounds for the
f
-divergence between two probabilistic programs. Our results allow us to revisit the foundations of differential privacy under a new light, and to pave the way for applications that use different instances of
f
-divergences.