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2018 | OriginalPaper | Chapter

Constrained-Based Differential Privacy: Releasing Optimal Power Flow Benchmarks Privately

Releasing Optimal Power Flow Benchmarks Privately

Authors : Ferdinando Fioretto, Pascal Van Hentenryck

Published in: Integration of Constraint Programming, Artificial Intelligence, and Operations Research

Publisher: Springer International Publishing

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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.

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Title: Constrained-Based Differential Privacy: Releasing Optimal Power Flow Benchmarks Privately
Authors: Ferdinando Fioretto
Pascal Van Hentenryck
Publisher: Springer International Publishing
Book: Integration of Constraint Programming, Artificial Intelligence, and Operations Research
Print ISBN: 978-3-319-93030-5

Electronic ISBN: 978-3-319-93031-2

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-93031-2_15

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