# Information, Privacy and Stability in Adaptive Data Analysis

arXiv: https://arxiv.org/abs/1706.00820 · HTML: https://mlsanity.com/paper/1706.00820/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2017.

## Built on by (most cited first, 4 of 4)

- [Subsampling Suffices for Adaptive Data Analysis](https://mlsanity.com/paper/2302.08661/) (2023)
- [Adaptive Data Analysis in a Balanced Adversarial Model](https://mlsanity.com/paper/2305.15452/) (2023)
- [Fingerprinting Codes Meet Geometry: Improved Lower Bounds for Private Query Release and Adaptive Data Analysis](https://mlsanity.com/paper/2412.14396/) (2024)
- [Efficient Adaptive Data Analysis over Dense Distributions](https://mlsanity.com/paper/2602.07732/) (2026)

## Abstract (arXiv)

Traditional statistical theory assumes that the analysis to be performed on a given data set is selected independently of the data themselves. This assumption breaks downs when data are re-used across analyses and the analysis to be performed at a given stage depends on the results of earlier stages. Such dependency can arise when the same data are used by several scientific studies, or when a single analysis consists of multiple stages. How can we draw statistically valid conclusions when data are re-used? This is the focus of a recent and active line of work. At a high level, these results show that limiting the information revealed by earlier stages of analysis controls the bias introduced in later stages by adaptivity. Here we review some known results in this area and highlight the role of information-theoretic concepts, notably several one-shot notions of mutual information.