Supporting Business Privacy Protection in Wireless Sensor Networks

With the pervasive use of wireless sensor networks (WSNs) within commercial environments, business privacy leakage due to the exposure of sensitive information transmitted in a WSN has become a major issue for enterprises. We examine business privacy protection in the application of WSNs. We propose a business privacy-protection system (BPS) that is modeled as a hierarchical profile in order to filter sensitive information with respect to enterprise-specified privacy requirements. The BPS aims at solving a tradeoff between metrics that are defined to estimate the utility of information and the business privacy risk. We design profile, risk assessment, and filtration agents to implement the BPS based on multiagent technology. The effectiveness of our proposed BPS is validated by experiments

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)\u2014 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\u2019s 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