We present a relational MPC framework for secure collaborative analytics on private data with no information leakage. Our work targets challenging use cases where data owners may not have private resources to participate in the computation, thus, they need to securely outsource the data analysis to untrusted third parties. We define a set of oblivious operators, explain the secure primitives they rely on, and analyze their costs in terms of operations and inter-party communication. We show how these operators can be composed to form end-to-end oblivious queries, and we introduce logical and physical optimizations that dramatically reduce the space and communication requirements during query execution, in some cases from quadratic to linear or from linear to logarithmic with respect to the cardinality of the input. We implement our framework on top of replicated secret sharing in a system called Secrecy and evaluate it using real queries from several MPC application areas. Our experiments demonstrate that the proposed optimizations can result in over 1000x lower execution times compared to baseline approaches, enabling Secrecy to outperform state-of-the-art frameworks and compute MPC queries on millions of input rows with a single thread per party.
翻译:我们为私人数据提供了一个相关的MPC框架,用于在没有信息泄漏的情况下对私人数据进行安全的协作分析。我们的工作目标是对数据所有者可能没有私人资源参与计算工作的使用案例提出挑战,因此,他们需要将数据分析安全外包给不信任的第三方。我们定义了一组隐蔽的操作者,解释了他们所依赖的安全原始操作者,并分析了他们在操作和当事方间通信方面的成本。我们展示了这些操作者如何组成成终端到终端的不记账查询,我们引入了逻辑和物理优化,在查询执行期间大大降低了空间和通信要求,在某些情况下,从二次到线性或线性到线性,从线性到线性,从输入的基点。我们用一个称为保密的系统在复制的秘密共享顶端执行我们的框架,并利用几个MPC应用领域的实际查询对它进行评估。我们的实验表明,与基线方法相比,拟议的优化可以导致1 000x次以上的执行时间,使Secrecy 大大地减少了在查询执行过程中的空间和通信要求,在某些情况下,从二次到线性到线性到对输入的要点进行对比。我们将MPC对数百万个输入行的查询。