TY - GEN
T1 - Differentially private distributed optimization
AU - Huang, Zhenqi
AU - Mitra, Sayan
AU - Vaidya, Nitin
N1 - Publisher Copyright:
Copyright 2015 ACM.
PY - 2015/1/4
Y1 - 2015/1/4
N2 - In distributed optimization and iterative consensus literature, a standard problem is for N agents to minimize a function f over a subset of Euclidean space, where the cost function is expressed as a sum ∑ fi. In this paper, we study the private distributed optimization problem (PDOP) with the additional requirement that the cost function of the individual agents should remain differentially private. The adversary attempts to infer information about the private cost functions from the messages that the agents exchange. Achieving differential privacy requires that any change of an individual's cost function only results in unsubstantial changes in the statistics of the messages. We propose a class of iterative algorithms for solving PDOP, which achieves differential privacy and convergence to a common value. Our analysis reveals the dependence of the achieved accuracy and the privacy levels on the the parameters of the algorithm. We observe that to achieve ε-differential privacy the accuracy of the algorithm has the order of O(1/ε2).
AB - In distributed optimization and iterative consensus literature, a standard problem is for N agents to minimize a function f over a subset of Euclidean space, where the cost function is expressed as a sum ∑ fi. In this paper, we study the private distributed optimization problem (PDOP) with the additional requirement that the cost function of the individual agents should remain differentially private. The adversary attempts to infer information about the private cost functions from the messages that the agents exchange. Achieving differential privacy requires that any change of an individual's cost function only results in unsubstantial changes in the statistics of the messages. We propose a class of iterative algorithms for solving PDOP, which achieves differential privacy and convergence to a common value. Our analysis reveals the dependence of the achieved accuracy and the privacy levels on the the parameters of the algorithm. We observe that to achieve ε-differential privacy the accuracy of the algorithm has the order of O(1/ε2).
KW - Differential privacy
KW - Distributed optimization
KW - Iterative consensus
UR - http://www.scopus.com/inward/record.url?scp=84978704861&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84978704861&partnerID=8YFLogxK
U2 - 10.1145/2684464.2684480
DO - 10.1145/2684464.2684480
M3 - Conference contribution
AN - SCOPUS:84978704861
T3 - ACM International Conference Proceeding Series
BT - ICDCN 2015 - Proceedings of the 16th International Conference on Distributed Computing and Networking
PB - Association for Computing Machinery
T2 - 16th International Conference on Distributed Computing and Networking, ICDCN 2015
Y2 - 4 January 2015 through 7 January 2015
ER -