Privacy-preserving Data Aggregation Based on the P-function Set in Wireless Sensor Networks

In-network data aggregation presents a critical challenge for data privacy in resource constraint wireless sensor networks. Existing schemes based on local collaboration have unfavourable communication cost, and some other schemes based on secret sharing with the sink are low resistant to data loss. To address these issues, we propose a PAPF scheme, in which a novel p-function set taking advantage of the algebraic properties of modular operation is constructed. Thanks to the p-functions, nodes can perturb their privacy data without extra data exchange, and the aggregation result can be recovered from the perturbed data in the cluster head. Extensive analysis and simulations show that PAPF scheme is able to preserve privacy more efficiently while consuming less communication overhead, and has a good resistance to data loss.