There has been a recent effort in applying differential privacy on memory access patterns to enhance data privacy. This is called differential obliviousness. Differential obliviousness is a promising direction because it provides a principled trade-off between performance and desired level of privacy. To date, it is still an open question whether differential obliviousness can speed up database processing with respect to full obliviousness. In this paper, we present the design and implementation of three new major database operators: selection with projection, grouping with aggregation, and foreign key join. We prove that they satisfy the notion of differential obliviousness. Our differentially oblivious operators have reduced cache complexity, runtime complexity, and output size compared to their state-of-the-art fully oblivious counterparts. We also demonstrate that our implementation of these differentially oblivious operators can outperform their state-of-the-art fully oblivious counterparts by up to $7.4\times$.

翻译：近期，学术界致力于将差分隐私应用于内存访问模式以增强数据隐私，这一方向被称为差分oblivious（differential obliviousness）。差分oblivious是一项颇具前景的研究方向，因为它能在性能与隐私保护水平之间提供原则性的权衡。迄今为止，差分oblivious能否相较于完全oblivious（full obliviousness）加速数据库处理仍是一个开放性问题。本文提出并实现了三种新型数据库运算符：带投影的选择运算、带聚合的分组运算以及外键连接运算，并证明它们满足差分oblivious性质。与当前最先进的完全oblivious运算符相比，我们的差分oblivious运算符在缓存复杂度、运行时复杂度和输出规模方面均有显著降低。实验表明，这些差分oblivious运算符的实际性能可超越现有最先进的完全oblivious运算符高达$7.4\times$。