一句话：提出将策略无关的 MPC 程序变换为语义等价且静态实施隐私策略的版本，并在新语言 Taypsi 中实现，消除动态检查开销。
安全多方计算（MPC）允许各方在不共享私有数据的前提下联合计算，但现有 MPC 语言通常要求用户把程序逻辑与隐私关注混写在一起，导致隐私策略难以修改与审计。此前的策略无关（policy-agnostic）MPC 语言依赖动态实施来解耦隐私要求与程序逻辑，然而由此产生的运行时开销使处理结构化数据的 MPC 应用难以扩展。本文提出通过程序变换消除该开销：将程序转换为语义等价的版本，在其中静态实施用户给定的隐私策略。作者将该方法实现为新的 MPC 语言 Taypsi，并在涉及结构化数据与复杂隐私策略的多种 MPC 应用上进行了实验评估，结果表明系统取得了显著的性能提升。
Secure multiparty computation (MPC) techniques enable multiple parties to compute joint functions over their private data without sharing that data with other parties, typically by employing powerful cryptographic protocols to protect individual's data. One challenge when writing such functions is that most MPC languages force users to intermix programmatic and privacy concerns in a single application, making it difficult to change or audit a program's underlying privacy policy. Prior policy-agnostic MPC languages relied on dynamic enforcement to decouple privacy requirements from program logic. Unfortunately, the resulting overhead makes it difficult to scale MPC applications that manipulate structured data. This work proposes to eliminate this overhead by instead transforming programs into semantically equivalent versions that statically enforce user-provided privacy policies. We have implemented this approach in a new MPC language, called Taypsi; our experimental evaluation demonstrates that the resulting system features considerable performance improvements on a variety of MPC applications involving structured data and complex privacy policies.
@article{paperbot2562,
title = {Taypsi: Static Enforcement of Privacy Policies for Policy-Agnostic Oblivious Computation},
author = {Qianchuan Ye and Benjamin Delaware},
journal = {Proceedings of the ACM on Programming Languages},
volume = {8},
number = {OOPSLA1},
year = {2024},
doi = {10.1145/3649861}
}