# The Target-Charging Technique for Privacy Accounting across Interactive Computations

arXiv: https://arxiv.org/abs/2302.11044 · HTML: https://mlsanity.com/paper/2302.11044/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2023.

## Built on by (most cited first, 2 of 2)

- [Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries](https://mlsanity.com/paper/2403.00028/) (2024)
- [Private Learning of Littlestone Classes, Revisited](https://mlsanity.com/paper/2510.00076/) (2025)

## Abstract (arXiv)

We propose the 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 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.