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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >Convex Optimisation-Based Privacy-Preserving Distributed Average Consensus in Wireless Sensor Networks
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Convex Optimisation-Based Privacy-Preserving Distributed Average Consensus in Wireless Sensor Networks

机译：基于Convex优化的隐私保留在无线传感器网络中的分布式平均共识

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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 optimization-based 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.
机译：在无线传感器网络的许多应用中，重要的是保护网络节点的隐私。 因此，隐私保留算法最近收到了非常关注。 在本文中，我们提出了一种新的凸优化的解决方案，对隐私保留的分布式平均共识问题。 所提出的方法基于乘法器（PDMM）的原始方法（PDMM），并且我们表明PDMM的引入的双变量将仅收敛于图形拓扑结构确定的某个子空间，并且不会在正交补充中收敛。 这些属性被利用以保护私人数据透露给他人。 更具体地，已证明所提出的算法对于被动和窃听的对手模型来确保安全。 最后，通过模拟证明了所提出方法的收敛性和准确性，表明该方法优于最先进的方法。

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