View source for Privacy Preserving Average Consensus

{{HTDB paper
| authors = Yilin Mo and Richard M. Murray
| title = Privacy Preserving Average Consensus
| source = 2014 Conference on Decision and Control (CDC)
| year = 2014
| type = Conference Paper
| funding = iCyPhy
| url = http://www.cds.caltech.edu/~murray/preprints/mm14-cdc.pdf
| abstract = 
Average consensus is a widely used algorithm for distributed averaging, where all the agents in the network constantly communicate and update their states in order to achieve an agreement. This approach could result in an undesirable disclosure of information on the initial state of agent i to other agents. In this paper, we propose a Privacy Preserving Average Consensus (PPAC) algorithm to guarantee the privacy of the initial state and the convergence to the exact initial values, by adding and subtracting random noises to the consensus process. We characterize the mean square convergence rate of the PPAC algorithm and derive upper and lower bounds for the covariance matrix of the maximum likelihood estimate on the initial state. We further provide an algebraic condition under which the PPAC algorithm is (epsilon, delta)-differentially private. A numerical example is provided to illustrate the effectiveness of the PPAC algorithm.

| flags = 
| filetype = PDF
| filesize = 648K
| tag = mm14-cdc
| id = 2014d
}}