Revisiting Private Stream Aggregation: Lattice-Based PSA

Abstract

In this age of massive data gathering for purposes of personalization, targeted ads, etc. there is an increased need for technology that allows for data analysis in a privacy-preserving manner. Private Stream Aggregation as introduced by Shi et al. (NDSS 2011) allows for the execution of aggregation operations over privacy-critical data from multiple data sources without placing trust in the aggregator and while maintaining differential privacy guarantees. We propose a generic PSA scheme, LaPS, based on the Learning With Error problem, which allows for a flexible choice of the utilized privacy-preserving mechanism while maintaining post-quantum security. We overcome the limitations of earlier schemes by relaxing previous assumptions in the security model and provide an efficient and compact scheme with high scalability. Our scheme is practical, for a plaintext space of 216 and 1000 participants we achieve a performance gain in decryption of roughly 150 times compared to previous results in Shi et al. (NDSS 2011).