Collusion-Tolerable and Efficient Privacy-Preserving Time-Series Data Aggregation Protocol

Abstract

Many miraculous ideas have been proposed to deal with the privacy-preserving time-series data aggregation problem in pervasive computing applications, such as mobile cloud computing. The main challenge consists in computing the global statistics of individual inputs that are protected by some confidentiality mechanism. However, those works either suffer from collusive attack or require time-consuming initialization at every aggregation request. In this paper, we proposed an efficient aggregation protocol which tolerates up to passive adversaries that do not try to tamper the computation. The proposed protocol does not require a trusted key dealer and needs only one initialization during the whole time-series data aggregation. We formally analyzed the security of our protocol and results showed that the protocol is secure if the Computational Diffie-Hellman (CDH) problem is intractable. Furthermore, the implementation showed that the proposed protocol can be efficient for the time-series data aggregation.