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Beyond Differential Privacy: Composition Theorems and Relational Logic for f-divergences between Probabilistic Programs

  • Gilles Barthe
  • Federico Olmedo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7966)

Abstract

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.

Keywords

Relative Entropy Advance Encryption Standard Relational Logic Statistical Distance Probabilistic Program 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gilles Barthe
    • 1
  • Federico Olmedo
    • 1
  1. 1.IMDEA Software InstituteMadridSpain

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