Constrained-based differential privacy: Releasing optimal power flow benchmarks privately

Ferdinando Fioretto, Pascal Van Hentenryck

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

This paper considers the problem of releasing optimal power flow benchmarks that maintain the privacy of customers (loads) using the notion of Differential Privacy. It is motivated by the observation that traditional differential-privacy mechanisms are not accurate enough: The added noise fundamentally changes the nature of the underlying optimization and often leads to test cases with no solution. To remedy this limitation, the paper introduces the framework of Constraint-Based Differential Privacy (CBDP) that leverages the post- processing immunity of differential privacy to improve the accuracy of traditional mechanisms. More precisely, CBDP solves an optimization problem to satisfies the problem-specific constraints by redistributing the noise. The paper shows that CBDP enjoys desirable theoretical properties and produces orders of magnitude improvements on the largest set of test cases available.

Original languageEnglish (US)
Title of host publicationIntegration of Constraint Programming, Artificial Intelligence, and Operations Research - 15th International Conference, CPAIOR 2018, Proceedings
EditorsWillem -Jan van Hoeve
PublisherSpringer Verlag
Pages215-231
Number of pages17
ISBN (Print)9783319930305
DOIs
StatePublished - Jan 1 2018
Externally publishedYes
Event15th International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2018 - Delft, Netherlands
Duration: Jun 26 2018Jun 29 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10848 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2018
CountryNetherlands
CityDelft
Period6/26/186/29/18

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Fioretto, F., & Van Hentenryck, P. (2018). Constrained-based differential privacy: Releasing optimal power flow benchmarks privately. In W. -J. van Hoeve (Ed.), Integration of Constraint Programming, Artificial Intelligence, and Operations Research - 15th International Conference, CPAIOR 2018, Proceedings (pp. 215-231). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10848 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-93031-2_15