TY - GEN
T1 - Constrained-based differential privacy
T2 - 15th International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2018
AU - Fioretto, Ferdinando
AU - Van Hentenryck, Pascal
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85048602612&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048602612&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-93031-2_15
DO - 10.1007/978-3-319-93031-2_15
M3 - Conference contribution
AN - SCOPUS:85048602612
SN - 9783319930305
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 215
EP - 231
BT - Integration of Constraint Programming, Artificial Intelligence, and Operations Research - 15th International Conference, CPAIOR 2018, Proceedings
A2 - van Hoeve, Willem -Jan
PB - Springer Verlag
Y2 - 26 June 2018 through 29 June 2018
ER -