We propose the \emph{Target Charging Technique} (TCT), a unified privacy accounting framework for interactive settings where a sensitive dataset is accessed multiple times using differentially private algorithms. Unlike traditional composition, where privacy guarantees deteriorate quickly with the number of accesses, TCT allows computations that don't hit a specified \emph{target}, often the vast majority, to be essentially free (while incurring instead a small overhead on those that do hit their targets). TCT generalizes tools such as the sparse vector technique and top-$k$ selection from private candidates and extends their remarkable privacy accounting benefits from noisy Lipschitz functions to general private algorithms.

翻译：我们提出了“目标计费技术”（Target Charging Technique, TCT），这是一种统一隐私核算框架，适用于敏感数据集通过差分隐私算法被多次访问的交互式场景。与传统的组合方法不同（其中隐私保证随访问次数增加而迅速恶化），TCT允许未达到指定“目标”的计算（通常是绝大多数情况）基本免费（仅对达到目标的计算施加少量开销）。TCT泛化了诸如稀疏向量技术和从私有候选项中选取top-k的选择工具，并将其显著的隐私核算优势从带噪声的Lipschitz函数扩展至通用隐私算法。