2015 American Control Conference (ACC) 2015
DOI: 10.1109/acc.2015.7170902
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Abstract: We present an optimization framework that solves constrained multi-agent optimization problems while keeping each agent's state differentially private. The agents in the network seek to optimize a local objective function in the presence of global constraints. Agents communicate only through a trusted cloud computer and the cloud also performs computations based on global information. The cloud computer modifies the results of such computations before they are sent to the agents in order to guarantee that the … Show more

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Cited by 55 publications
(16 citation statements)
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“…It is worth noting that there exists some differential-privacy based optimization approaches which are able to converge to the accurate optimization result in the mean-square sense, e.g. [22], [23]. However, those results require the assistance of a third party such as a cloud [22], [23], and therefore cannot be applied to the completely decentralized setting where no third parties exist.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…It is worth noting that there exists some differential-privacy based optimization approaches which are able to converge to the accurate optimization result in the mean-square sense, e.g. [22], [23]. However, those results require the assistance of a third party such as a cloud [22], [23], and therefore cannot be applied to the completely decentralized setting where no third parties exist.…”
Section: Introductionmentioning
confidence: 99%
“…[22], [23]. However, those results require the assistance of a third party such as a cloud [22], [23], and therefore cannot be applied to the completely decentralized setting where no third parties exist. Encryption-based approaches are also commonly used to enable privacy-preservation [24]- [26].…”
Section: Introductionmentioning
confidence: 99%
“…Some algorithms [28][29][30] insert noise having a geometrically decreasing variance over iterations and guarantee that the inserted noise adds up to zero. Some other algorithms [31][32][33] rely on a trusted third party to obtain the zero-sum property. However, a trusted third party is hard to implement in ad hoc networks including also many WSNs.…”
Section: Introductionmentioning
confidence: 99%
“…In [11], parameters of individual constraints in resource allocation problems is kept private. Preserving the privacy of decisions and cost functions in distributed optimizations is investigated in [28,29]. Applications of differential privacy can also be found in other related problems, such as machine learning [30,31], mechanism design [32,33], and transportation systems [34,35].…”
Section: Introductionmentioning
confidence: 99%