Preserving privacy against external and internal threats in WSN data aggregation

In this paper, we propose two efficient and privacy-preserving data aggregation protocols for WSNs: PASKOS (Privacy preserving based on Anonymously Shared Keys and Omniscient Sink) and PASKIS (Pri- vacy preserving based on Anonymously Shared Keys and Ignorant Sink)— requiring low overhead. Both protocols guarantee privacy preservation and a high data-loss resilience. In particular, PASKOS effectively pro- tects the privacy of any node against other nodes, by requiring O(log N ) communication cost in the worst case and O(1) on average, and O(1) as for memory and computation. PASKIS can even protect a node’s privacy against a compromised sink, requiring only O(1) overhead as for compu- tation, communication, and memory; however, these gains in efficiency are traded-off with a (slightly) decrease in the assured level of privacy. A thorough analysis and extensive simulations demonstrate the supe- rior performance of our protocols against existing solutions in terms of privacy-preserving effectiveness, efficiency, and accuracy of computed ag- gregation.