AI 中文总结
研究提出统一分析引擎CryptDough，支持多参与方安全分析，通过分层设计与虚拟向量概念提升扩展性，性能优于现有系统2倍以上。
AI 中文摘要
我们提出CryptDough，这是一个用于安全多方计算（MPC）的统一分析引擎。CryptDough使多个互不信任的参与方能够基于各自的私有输入共同执行数据分析流水线，且除结果（如聚合统计量）外不会泄露任何其他信息。与现有仅支持单一威胁模型或工作负载类型的MPC解决方案不同，CryptDough在同一系统运行时内，内置支持跨域分析（关系型、时间序列、机器学习推理），涵盖多种威胁模型。CryptDough的贡献包括：（i）分层系统设计，通过逐步降低抽象层级促进模块化与可扩展性；（ii）虚拟向量概念，使用户可在软件栈各层编写单线程代码，而将通信、并行化及内存管理的复杂度下放至执行引擎。我们表明，CryptDough可泛化最先进MPC系统的功能，且在其支持的分析任务上仍具竞争力，通常性能优于这些系统2倍以上。
英文摘要
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 $2\times$.
