Paper 2024/1433

$Shortcut$: Making MPC-based Collaborative Analytics Efficient on Dynamic Databases

Abstract

Secure Multi-party Computation (MPC) provides a promising solution for privacy-preserving multi-source data analytics. However, existing MPC-based collaborative analytics systems (MCASs) have unsatisfying performance for scenarios with dynamic databases. Naively running an MCAS on a dynamic database would lead to significant redundant costs and raise performance concerns, due to the substantial duplicate contents between the pre-updating and post-updating databases. In this paper, we propose $Shortcut$, a framework that can work with MCASs to enable efficient queries on dynamic databases that support data insertion, deletion, and update. The core idea of $Shortcut$ is to materialize previous query results and directly update them via our query result update (QRU) protocol to obtain current query results. We customize several efficient QRU protocols for common SQL operators, including Order-by-Limit, Group-by-Aggregate, Distinct, Join, Select, and Global Aggregate. These protocols are composable to implement a wide range of query functions. In particular, we propose two constant-round protocols to support data insertion and deletion. These protocols can serve as important building blocks of other protocols and are of independent interest. They address the problem of securely inserting/deleting a row into/from an ordered table while keeping the order. Our experiments show that $Shortcut$ outperforms naive MCASs for minor updates arriving in time, which captures the need of many realistic applications (e.g., insurance services, account data management). For example, for a single query after an insertion, $Shortcut$ achieves up to $186.8 \times$ improvement over those naive MCASs without our QRU protocols on a dynamic database with $2^{16} \sim 2^{20}$ rows, which is common in real-life applications.