# Privately Answering Queries on Skewed Data via Per Record Differential Privacy

arXiv: https://arxiv.org/abs/2310.12827 · HTML: https://mlsanity.com/paper/2310.12827/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2023.

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

- [Risk-Equalized Differentially Private Synthetic Data: Protecting Outliers by Controlling Record-Level Influence](https://mlsanity.com/paper/2602.10232/) (2026)

## Abstract (arXiv)

We consider the problem of the private release of statistics (like aggregate payrolls) where it is critical to preserve the contribution made by a small number of outlying large entities. We propose a privacy formalism, per-record zero concentrated differential privacy (PzCDP), where the privacy loss associated with each record is a public function of that record's value. Unlike other formalisms which provide different privacy losses to different records, PRzCDP's privacy loss depends explicitly on the confidential data. We define our formalism, derive its properties, and propose mechanisms which satisfy PRzCDP that are uniquely suited to publishing skewed or heavy-tailed statistics, where a small number of records contribute substantially to query answers. This targeted relaxation helps overcome the difficulties of applying standard DP to these data products.