PDA: Semantically Secure Time-Series Data Analytics with Dynamic User Groups

Abstract— Third-party analysis on private records is becoming increasingly important due to the widespread data collection for various analysis purposes. However, the data in its original form often contains sensitive information about individuals, and its publication will severely breach their privacy. In this paper, we present a novel Privacy-preserving Data Analytics frameworkPDA, which allows a third-party aggregator to obliviously conduct many different types of polynomial-based analysis on private data records provided by a dynamic sub-group of users. Notably, every user needs to keep onlyO(n)keys to join data analysis amongO(2n)different groups of users, and any data analysis that is represented by polynomials is supported by our framework. Besides, a real implementation shows the performance of our framework is comparable to the peer works who present ad-hoc solutions for specific data analysis applications. Despite such nice properties ofPDA, it is provably secure against a very powerful attacker (chosen-plaintext attack) even in the Dolev-Yao network model where all communication channels are insecure. We focus on a scenario that is slightly different from the privacy-preserving data publishing or regulation enforcement. We invetigate the privacy-sensitive third-party data analysis scenario where a third-party aggregator wants to perform analysis on a private dataset generated and possessed by users, but users do not want to release their individual data records to anyone else. < final year projects >