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Privacy-preserving Data Aggregation Scheme Based on the P-Function Set in Wireless Sensor Networks
Weini Zeng, Yaping Lin, Jianping Yu, Shiming He and Lei Wang

In-network data aggregation presents a critical challenge for data privacy in resource constraint wireless sensor networks. Existing schemes based on local collaboration have unfavorable communication cost, and some other schemes based on secret sharing with the sink have low resistance to data loss. To address these issues, a privacy-preserving aggregation scheme based on the P-function set (PAPF) was proposed, in which a novel P-function set taking advantage of the algebraic properties of congruence is constructed. In P-functions, nodes can perturb their private data without the extra data exchange, and the aggregation result can be recovered from the perturbed data in the cluster head. Due to the flexible generation of the P-function set, the PAPF scheme is not only adapted to the periodic reporting of nodes and query response reports of the sink, but also to the adding and inaction of nodes. Extensive analysis and simulations show that the PAPF scheme is able to preserve privacy more efficiently while consuming less communication resource, and has a good resistance to data loss.

Keywords: Wireless sensor networks; data privacy; data aggregation; p-function set.

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