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.

翻译：本文提出了一类用于线性查询（特别是范围查询）的局部差分隐私机制，该机制利用相关输入扰动，同时实现无偏性、一致性、统计透明性，并能根据特定查询边界或分层数据库结构隐含的精度目标控制效用需求。所提出的级联采样算法能够精确且高效地实现该机制。理论界表明，我们能在获得接近最优效用的同时，在实证上与输出扰动方法具有竞争力。