Paper 2026/1320

Differentially Private Intermediate Result Resizing for Scalable Secure Multi-Party Analytics

Abstract

Secure Multi-Party Computation (MPC) enables collaborative analytics without exposing raw data. Yet, complex data retrieval operations in relational databases (i.e., relational queries) remain limited by scalability bottlenecks: oblivious operators must pad intermediate results to worst-case sizes to prevent information leakage, thereby inflating communication and computation costs. We propose Resizer, a lightweight operator that can be inserted transparently into relational query plans to bound intermediate result sizes while preserving rigorous privacy guarantees. Resizer reduces intermediate results from fully-oblivious to noisy size bounds that satisfy differential privacy (DP). We propose two Resizer variants, shuffle-based and sort-based, and provide a formal analysis of the privacy guarantees and security in the semi-honest model. We evaluate the Resizer variants in the ORQ framework under semi-honest and honest-majority assumptions using TPC-H benchmark queries, demonstrating that Resizer incurs negligible overhead while reducing data volume. In both LAN and WAN settings, complex queries achieve speedups compared with state-of-the-art oblivious baselines, confirming that Resizer reduces the padding bottleneck and enables scalable, privacy-preserving relational analytics.