CryptDough: A Unified Analytics Engine for Secure Multiparty Computation

2026-08-18

AI Summary

为解决多方安全计算中跨领域分析问题，提出CryptDough引擎，通过层次化设计和虚拟向量技术，在保证隐私的同时提高性能。

Abstract

We present CryptDough, a unified analytics engine for secure multiparty computation (MPC). CryptDough enables multiple distrusting parties to jointly execute a data analysis pipeline on their private inputs and learn nothing beyond the result (e.g., aggregate statistics). Unlike existing MPC solutions that support a single threat model or workload type, CryptDough provides built-in support for cross-domain analytics (relational, time series, ML inference) under various threat models, all within the same system runtime. CryptDough contributes (i) a hierarchical system design that facilitates modularity and extensibility through progressive lowering of abstractions, and (ii) the concept of virtual vectors that enable users to write single-threaded code across all layers of the software stack, while pushing the complexity of communication, parallelization, and memory management down to the execution engine. We show that CryptDough generalizes the functionality of state-of-the-art MPC systems and remains competitive on the analytics they support, often outperforming them by more than $2\times$.

Problem

• secure multiparty computation
• cross-domain analytics
• threat models

Innovation

• Unified Analytics Engine
• Secure Multiparty Computation
• Virtual Vectors
• Hierarchical System Design