Privacy preserving average consensus

By Yilin Mo, Richard M. Murray. IEEE Conference on Decision and Control, 2014

500 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/eae5dc03d21d057c4b34998caf05394811d7b90b

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In Artificial Intelligence

In Computer Networks and Communications

Citations in the corpus

ADMM Based Privacy-Preserving Decentralized Optimization (2017, background): “Other examples include the agreement problem [18], where a group of individuals want to reach consensus on a subject without leaking their individual opinions to others [17], and the regression problem [6], where individual agent’s training data may contain sensitive information (e.”
Secure and Privacy-Preserving Consensus (2017, methodology): “In this section, we show the advantages of the proposed average consensus approach over existing data-obfuscationbased average consensus approaches, more specifically, the decaying-noise approach by Mo and Murray [12], the finitenoise-sequence approach by Manitara and Hadjicostis [13], and the diffe”
Randomized Iterative Methods for Linear Systems: Momentum, Inexactness and Gossip (2019, methodology): “This approach is inspired by the works of [123, 124], and protects the initial values by inserting noise in the process.”
Differentially private average consensus: Obstructions, trade-offs, and optimal algorithm design (2015, methodology): “gent as well as all its neighbors, the privacy of that agent is preserved, in the sense that different initial conditions may produce the same transmitted messages. This idea is further developed in (Mo and Murray, 2014, 2015), where agents add an innitely-long exponentially-decaying zero-sum sequen”
A systems and control perspective of CPS security (2019, background): “An example of masking the private data in the presence of an adversarial agent is [95].”
Hiding in the Crowd: A Massively Distributed Algorithm for Private Averaging with Malicious Adversaries (2018, background): “We emphasize that in contrast to the lower bounds on the variance of the maximum likelihood estimate obtained by Mo and Murray (2014), our variance computation (6) is exact, interpretable and holds under non-uniform prior beliefs of the adversary.”