Paper 2020/1611

SLAP: Simple Lattice-Based Private Stream Aggregation Protocol

Jonathan Takeshita and Ryan Karl and Ting Gong and Taeho Jung

Abstract

Today, users' data is gathered and analyzed on a massive scale. While user data analytics such as personalized advertisement need to make use of this data, users may not wish to divulge their information without security and privacy guarantees. Private Stream Aggregation (PSA) allows the secure aggregation of time-series data, affording security and privacy to users' private data, which is much more efficient than general secure computation such as homomorphic encryption, multiparty computation, and secure hardware based approaches. Earlier PSA protocols face limitations including needless complexity or a lack of post-quantum security. In this work, we present SLAP, a lattice-based PSA protocol. SLAP features two variants with post-quantum security, with simpler and more efficient computations enabled by (1) the white- box approach that builds the encryption directly from the Ring Learning With Error assumption and (2) the state-of-the-art algorithmic optimization in lattice-based cryptography. We show that SLAP meets the security and privacy requirements of PSA, and show experimentally the improvements of SLAP over similar work. We show a speedup of 20.76x over the previous state-of-the-art lattice-based PSA work's aggregation, and apply techniques including RNS, NTT, and batching to obtain a throughput of over 600,000 aggregations per second.