Secure multiparty computation (MPC) techniques enable multiple parties to compute joint functions over their private data without sharing that data to 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. Existing policy-agnostic MPC languages rely on run-time / 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 to 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 polices.

翻译：安全多方计算（MPC）技术通过采用强大的密码学协议保护个人数据，使多个参与方能够在不共享私有数据的前提下，联合计算其私有数据上的函数。编写此类函数时的一个挑战在于，大多数MPC语言强制用户将程序逻辑与隐私关切混杂在单一应用中，导致难以修改或审计程序的底层隐私策略。现有的策略无关MPC语言依赖运行时/动态强制机制来解耦隐私需求与程序逻辑。然而，由此产生的开销使得处理结构化数据的MPC应用难以扩展。本文提出通过将程序转换为语义等价的版本，以消除此类开销，从而静态强制实施用户提供的隐私策略。我们在名为Taypsi的新型MPC语言中实现了该方法；实验评估表明，在处理涉及结构化数据与复杂隐私策略的多种MPC应用时，所构建系统性能显著提升。