Samenvatting

In many applications of wireless sensor networks, it is important
that the privacy of the nodes of the network be protected. Therefore,
privacy-preserving algorithms have received quite some attention
recently. In this paper, we propose a novel convex optimizationbased
solution to the problem of privacy-preserving distributed average
consensus. The proposed method is based on the primal-dual
method of multipliers (PDMM), and we show that the introduced
dual variables of the PDMM will only converge in a certain subspace
determined by the graph topology and will not converge in the
orthogonal complement. These properties are exploited to protect
the private data from being revealed to others. More specifically, the
proposed algorithm is proven to be secure for both passive and eavesdropping
adversary models. Finally, the convergence properties and
accuracy of the proposed approach are demonstrated by simulations
which show that the method is superior to the state-of-the-art.