We propose the \emph{Target Charging Technique} (TCT), a unified privacy analysis 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 enhancement benefits from noisy Lipschitz functions to general private algorithms.

翻译：我们提出*目标收费技术*（TCT），一种针对交互式场景的统一隐私分析框架，在该场景中敏感数据集会通过差分隐私算法被多次访问。与传统组合方法（即随着访问次数增加隐私保证快速恶化）不同，TCT允许未命中指定*目标*的计算（通常占绝大多数）基本不计入隐私成本（仅对命中目标的计算施加微小开销）。TCT将稀疏向量技术、私密候选者中前k项选择等工具的隐私增强优势，从带噪声的Lipschitz函数泛化至通用私密算法。