# ProBE: Proportioning Privacy Budget for Complex Exploratory Decision Support

arXiv: https://arxiv.org/abs/2406.15655 · HTML: https://mlsanity.com/paper/2406.15655/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2024.

## Built on by (most cited first, 1 of 1)

- [Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents](https://mlsanity.com/paper/2606.26627/) (2026)

## Abstract (arXiv)

This paper studies privacy in the context of complex decision support queries composed of multiple conditions on different aggregate statistics combined using disjunction and conjunction operators. Utility requirements for such queries necessitate the need for private mechanisms that guarantee a bound on the false negative and false positive errors. This paper formally defines complex decision support queries and their accuracy requirements, and provides algorithms that proportion the existing budget to optimally minimize privacy loss while supporting a bounded guarantee on the accuracy. Our experimental results on multiple real-life datasets show that our algorithms successfully maintain such utility guarantees, while also minimizing privacy loss.