Privacy-Preserving and Fault-Tolerant Aggregation of Time-Series Data with a Semi-Trusted Authority

Time-series data aggregation in Internet of Things applications is a useful operation, where the time-series data is sensed by a group of users, and gathered by the aggregator for real-Time analysis. However, some security and privacy challenges still affect the collection and aggregation process. Although existing privacy-preserving solutions achieve strong privacy guarantees, they introduce a fully trusted TA that is difficult to realize in the real world. Besides, they cannot be directly applied in time-series data aggregation scenarios due to unacceptable efficiency. In this article, we propose a privacy-preserving time-series data aggregation scheme with a semi-Trusted authority. Moreover, our scheme also supports arbitrary aggregate functions and fault tolerance to enhance the reliability and scalability of data aggregation. Security analysis demonstrates that our proposed scheme achieves (n-k)-source anonymity even if k(k\leq (n-2)) data providers collude with the cloud server. We also conduct thorough experiments based on a simulated data aggregation scenario to show the high computation and communication efficiency of our scheme.