Privacy-Preserving Distributed Optimisation using Stochastic PDMM
Privacy-preserving distributed processing has received considerable attention recently. The main purpose of these algorithms is to solve certain signal processing tasks over a network in a decentralised fashion without revealing private/secret data to the outside world. Because of the iterative natu...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
13.12.2023
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Online Access | Get full text |
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Summary: | Privacy-preserving distributed processing has received considerable attention
recently. The main purpose of these algorithms is to solve certain signal
processing tasks over a network in a decentralised fashion without revealing
private/secret data to the outside world. Because of the iterative nature of
these distributed algorithms, computationally complex approaches such as
(homomorphic) encryption are undesired. Recently, an information theoretic
method called subspace perturbation has been introduced for synchronous update
schemes. The main idea is to exploit a certain structure in the update
equations for noise insertion such that the private data is protected without
compromising the algorithm's accuracy. This structure, however, is absent in
asynchronous update schemes. In this paper we will investigate such
asynchronous schemes and derive a lower bound on the noise variance after
random initialisation of the algorithm. This bound shows that the privacy level
of asynchronous schemes is always better than or at least equal to that of
synchronous schemes. Computer simulations are conducted to consolidate our
theoretical results. |
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DOI: | 10.48550/arxiv.2312.08144 |