Privacy-Preserving and Customization-Supported Data Aggregation in Mobile Crowdsensing

Data aggregation is a fundamental problem in mobile crowdsensing (MCS). However, the existing approaches are still unsatisfactory considering the privacy protection of sensing data and aggregation results. In addition, most existing privacy-preserving data aggregation schemes can only support a single type of aggregation, which limits their application scenarios. To address these issues, we propose a novel privacy-preserving and customization-supported data aggregation scheme that can achieve multiple types of aggregation. Specifically, we utilize additive secret sharing ( $\mathcal {ASS}$ ) to protect the privacy of both sensing data and aggregation results and then propose a simplified secure triplet generation protocol based on $\mathcal {ASS}$ to construct secure aggregation operations. Moreover, we design a secure comparison (SC) algorithm and a secure top- $K$ algorithm to realize customized aggregation (i.e., statistical aggregation over top- $K$ largest or smallest values of sensing data). The formal theoretical analysis demonstrates that the proposed scheme is effective, and the extensive experiments conducted on a real-world data set show that the proposed approach is privacy preserving and efficient.