ArcEDB: An Arbitrary-Precision Encrypted Database via (Amortized) Modular Homomorphic Encryption

Zhou Zhang, Song Bian, Zian Zhao, Ran Mao, Haoyi Zhou, Jiafeng Hua, Yier Jin, Zhenyu Guan

Abstract: Fully homomorphic encryption (FHE) based database outsourcing is drawing growing research interests. At its current state, there exist two primary obstacles against FHE-based encrypted databases (EDBs): i) low data precision, and ii) high computational latency. To tackle the precision-performance dilemma, we introduce ArcEDB, a novel FHE-based SQL evaluation infrastructure that simultaneously achieves high data precision and fast query evaluation. Based on a set of new plaintext encoding schemes, we are able to execute arbitrary-precision ciphertext-to-ciphertext homomorphic comparison orders of magnitude faster than existing methods. Meanwhile, we propose efficient conversion algorithms between the encoding schemes to support highly composite SQL statements, including advanced filter-aggregation and multi-column synchronized sorting. We perform comprehensive experiments to study the performance characteristics of ArcEDB. In particular, we show that ArcEDB can be up to $57\times$ faster in homomorphic filtering and up to $20\times$ faster over end-to-end SQL queries when compared to the state-of-the-art FHE-based EDB solutions. Using ArcEDB, a SQL query over a 10K-row time-series EDB with 64-bit timestamps only runs for under one minute.