Finite Approximate Consensus for Privacy in Distributed Sensor Networks

With concepts such as the Internet of Things becoming more commonplace, greater emphasis must be placed on data privacy in large-scale public networks for these to be used securely without the threat of data theft. Most current distributed processing research deals with improving the flexibility and convergence speed of algorithms for networks of finite size with no constraints on information sharing and no concept for expected levels of signal privacy. In this work we investigate the concept of data privacy in unbounded public networks, where processing approximation is seen as a means to restrict information travel. We describe a practical method to use during processing aggregation stages that may be implemented in hardware to restrict the distance that data is shared. This method is efficient to implement, and requires very few update iterations to perform. We simulate the method and demonstrate its performance for the task of distributed acoustic beamforming in microphone sensor networks.