跨交互式计算中用于隐私核算的目标充电技术
The Target-Charging Technique for Privacy Accounting across Interactive Computations
AI总结：
本文提出目标充电技术（TCT），通过让未命中指定目标的交互式计算几乎不消耗隐私预算，将稀疏向量技术和 top-k 选择的隐私增强优势推广到一般差分隐私算法，从而缓解多次访问下的隐私退化问题。
摘要：
我们提出“目标充电技术”（TCT），这是一个统一的隐私分析框架，适用于敏感数据集被多次访问且每次访问使用差分隐私算法的交互式场景。与传统的组合方式不同——在传统组合中，隐私保证会随着访问次数迅速恶化——TCT 允许那些未命中指定“目标”的计算（通常占绝大多数）基本免费，而对那些确实命中目标的计算则只产生少量额外开销。TCT 推广了稀疏向量技术和私有候选中 top-k 选择等工具，并将其显著的隐私增强优势从带噪 Lipschitz 函数扩展到一般私有算法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
