Scape: Scalable Collaborative Analytics System on Private Database with Malicious Security

Feng Han, Lan Zhang, Hanwen Feng, Weiran Liu, Xiang-Yang Li

Abstract: Many data applications can be facilitated or even spawned by joint analysis on databases held by different owners, but privacy concerns are currently the biggest hindrance. Though a practical privacy-preserving collaborative database analytics system is strongly desired, existing approaches do not support efficient queries for several essential SQL operators such as the general join, especially on large databases. In this paper, we propose, analyze, and implement Scape, a Scalable Collaborative Analytics system on Private databasE with malicious security. In Scape, databases from different parties are secretly shared to three non-colluding computing parties. Users can perform various SQL queries (including fully functional Join, Group by, Aggregation, etc.) on shared databases, and all entities learn nothing beyond their priori knowledge during the whole execution even when they deviate from protocols. At the heart of Scape lies several asymptotically efficient SQL protocols. Particularly, our general join protocol has O (n log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> n + m) communication/computation cost when joining two tables with o (n) rows to a table with 0 (m) rows, significantly outperforming the state-of-the-art approach with O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) cost. The benchmark results confirm the advantages of Scape, which is up to 25 x faster than the baseline.