Differentially private distributed optimization

Zhenqi Huang, Sayan Mitra, Nitin Vaidya

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


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).

Original languageEnglish (US)
Title of host publicationICDCN 2015 - Proceedings of the 16th International Conference on Distributed Computing and Networking
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450329286
StatePublished - Jan 4 2015
Event16th International Conference on Distributed Computing and Networking, ICDCN 2015 - Goa, India
Duration: Jan 4 2015Jan 7 2015

Publication series

NameACM International Conference Proceeding Series


Other16th International Conference on Distributed Computing and Networking, ICDCN 2015


  • Differential privacy
  • Distributed optimization
  • Iterative consensus

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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