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

Abstract

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 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {ASS}$ </tex-math></inline-formula> ) to protect the privacy of both sensing data and aggregation results and then propose a simplified secure triplet generation protocol based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {ASS}$ </tex-math></inline-formula> to construct secure aggregation operations. Moreover, we design a secure comparison (SC) algorithm and a secure top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> algorithm to realize customized aggregation (i.e., statistical aggregation over top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> 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.