ADMM Based Privacy-Preserving Decentralized Optimization

Privacy preservation is addressed for decentralized optimization, where <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> agents cooperatively minimize the sum of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> convex functions private to these individual agents. In most existing decentralized optimization approaches, participating agents exchange and disclose states explicitly, which may not be desirable when the states contain sensitive information of individual agents. The problem is more acute when adversaries exist which try to steal information from other participating agents. To address this issue, we propose a privacy-preserving decentralized optimization approach based on alternating direction method of multipliers (ADMM) and partially homomorphic cryptography. To the best of our knowledge, this is the first time that cryptographic techniques are incorporated in a fully decentralized setting to enable privacy preservation in decentralized optimization in the absence of any third party or aggregator. To facilitate the incorporation of encryption in a fully decentralized manner, we introduce a new ADMM, which allows time-varying penalty matrices and rigorously prove that it has a convergence rate of <inline-formula> <tex-math notation="LaTeX">$O(1/t)$ </tex-math></inline-formula>. Numerical and experimental results confirm the effectiveness and low-computational complexity of the proposed approach.

• Publication year

  2017

• Venue

  IEEE Transactions on Information Forensics and Security

• Publication date

  2017-07-13

• Fields of study

  Mathematics, Computer Science, Engineering

• Identifiers
  DOI 10.1109/TIFS.2018.2855169
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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