Differentially Private Range Queries with Correlated Input Perturbation
CoRR(2024)
摘要
This work proposes a class of locally differentially private mechanisms for
linear queries, in particular range queries, that leverages correlated input
perturbation to simultaneously achieve unbiasedness, consistency, statistical
transparency, and control over utility requirements in terms of accuracy
targets expressed either in certain query margins or as implied by the
hierarchical database structure. The proposed Cascade Sampling algorithm
instantiates the mechanism exactly and efficiently. Our bounds show that we
obtain near-optimal utility while being empirically competitive against output
perturbation methods.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要