In many practical applications of differential privacy, practitioners seek to provide the best privacy guarantees subject to a target level of accuracy. A recent line of work by \cite{LigettNeRoWaWu17, WhitehouseWuRaRo22} has developed such accuracy-first mechanisms by leveraging the idea of \emph{noise reduction} that adds correlated noise to the sufficient statistic in a private computation and produces a sequence of increasingly accurate answers. A major advantage of noise reduction mechanisms is that the analysts only pay the privacy cost of the least noisy or most accurate answer released. Despite this appealing property in isolation, there has not been a systematic study on how to use them in conjunction with other differentially private mechanisms. A fundamental challenge is that the privacy guarantee for noise reduction mechanisms is (necessarily) formulated as \emph{ex-post privacy} that bounds the privacy loss as a function of the released outcome. Furthermore, there has yet to be any study on how ex-post private mechanisms compose, which allows us to track the accumulated privacy over several mechanisms. We develop privacy filters \citep{RogersRoUlVa16, FeldmanZr21, WhitehouseRaRoWu22} that allow an analyst to adaptively switch between differentially private and ex-post private mechanisms subject to an overall privacy guarantee.

翻译：在许多差分隐私的实际应用中，从业者希望在达到目标准确度的前提下提供最佳的隐私保障。近期一系列研究（\cite{LigettNeRoWaWu17, WhitehouseWuRaRo22}）通过利用\textbf{噪声降低}方法开发了此类准确性优先机制，该方法在私有计算过程中向充分统计量添加相关噪声，并生成一系列精度递增的结果。这类机制的主要优势在于，分析师仅需为噪声最小（即最准确）的结果支付隐私成本。尽管这一特性本身具有吸引力，但尚未出现关于如何将其与其他差分隐私机制结合使用的系统性研究。根本性挑战在于，噪声降低机制的隐私保障（必须）被表述为\textbf{事后隐私}——即隐私损失作为发布结果的函数被约束。此外，目前仍缺乏关于事后隐私机制组合的研究，这使得我们无法追踪多个机制累积的隐私损失。我们开发了隐私过滤器（\cite{RogersRoUlVa16, FeldmanZr21, WhitehouseRaRoWu22}），允许分析师在确保整体隐私保障的前提下，在差分隐私机制与事后隐私机制之间进行自适应切换。