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Relational *-liftings for differential privacy

  • IMDEA Software Institute
  • MPI for Security and Privacy
  • Sorbonne Université
  • University of Wisconsin-Madison
  • Seikei University

Research output: Contribution to journalArticlepeer-review

Abstract

Recent developments in formal verification have identified approximate liftings (also known as approximate couplings) as a clean, compositional abstraction for proving differential privacy. This construction can be defined in two styles. Earlier definitions require the existence of one or more witness distributions, while a recent definition by Sato uses universal quantification over all sets of samples. These notions each have their own strengths: the universal version is more general than the existential ones, while existential liftings are known to satisfy more precise composition principles. We propose a novel, existential version of approximate lifting, called ⋆-lifting, and show that it is equivalent to Sato’s construction for discrete probability measures. Our work unifies all known notions of approximate lifting, yielding cleaner properties, more general constructions, and more precise composition theorems for both styles of lifting, enabling richer proofs of differential privacy. We also clarify the relation between existing definitions of approximate lifting, and consider more general approximate liftings based on f-divergences.

Original languageEnglish
Pages (from-to)18:1-18:32
JournalLogical Methods in Computer Science
Volume15
Issue number4
DOIs
Publication statusPublished - 1 Oct 2019

Keywords

  • Differential privacy
  • Max flow/min cut
  • Relational lifting
  • probabilistic

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