Abstract
Privacy-preserving data aggregation has been widely recognized as a key enabling functionality in wireless sensor networks to allow the base station to learn valuable statistics of the sensed data while protecting individual sensor node’s data privacy. Existing privacy-preserving data aggregation schemes all target simple statistic functions such as SUM, COUNT, and MAX/MIN. In contrast, a quantile summary allows a base station to extract the \(\phi \)-quantile for any \(0<\phi <1\) of all the sensor readings in the network and can thus provide a more accurate characterization of the data distribution. Unfortunately, how to realize privacy-preserving quantile summary aggregation remains an open challenge. In this paper, we introduce the design and evaluation of PrivQSA, a novel privacy-preserving quantile summary aggregation scheme for wireless sensor networks, which enables efficient quantile summary aggregation while guaranteeing \(\epsilon \)-Local Differential Privacy for individual sensors. Detailed simulation studies confirm the efficacy and efficiency of the proposed protocol.
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Acknowledgements
The authors would like to thank the anonymous reviewers for their constructive comments and helpful advice. This work was supported in part by the US National Science Foundation under grants CNS-1651954 (CAREER), CNS-1933047, and CNS-1718078.
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Aseeri, A., Zhang, R. (2022). Locally Differentially Private Quantile Summary Aggregation in Wireless Sensor Networks. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13757. Springer, Cham. https://doi.org/10.1007/978-3-031-21743-2_29
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