The Target-Charging Technique for Privacy Accounting across Interactive Computations

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.