差分隐私与次线性时间有时不可兼容
Differential privacy and Sublinear time are incompatible sometimes
AI总结：
该研究首次探索同时满足差分隐私与次线性时间的算法下界，证明一般情况下二者不可兼容，并以基于单向边际分布的简单问题为例验证了该结论。
摘要：
差分隐私与次线性算法都是大数据分析时代快速兴起的算法方向。尽管近期研究表明，针对图参数估计、聚类等诸多问题，已存在满足差分隐私的次线性算法，但人们对这类算法的难度结果仍知之甚少。本文首次对同时追求差分隐私与次线性时间的问题展开下界研究。我们的核心结果是，在一般情况下这两个目标不可兼容。具体而言，我们证明了一个基于单向边际分布的简单问题，既存在差分隐私算法，也存在次线性时间算法，但不存在同时满足差分隐私的“严格”次线性时间算法。
英文摘要：
Differential privacy and sublinear algorithms are both rapidly emerging algorithmic themes in times of big data analysis. Although recent works have shown the existence of differentially private sublinear algorithms for many problems including graph parameter estimation and clustering, little is known regarding hardness results on these algorithms. In this paper, we initiate the study of lower bounds for problems that aim for both differentially-private and sublinear-time algorithms. Our main result is the incompatibility of both the desiderata in the general case. In particular, we prove that a simple problem based on one-way marginals yields both a differentially-private algorithm, as well as a sublinear-time algorithm, but does not admit a ``strictly'' sublinear-time algorithm that is also differentially private.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
