# Practical and Ready-to-Use Methodology to Assess the re-identification Risk in Anonymized Datasets

arXiv: https://arxiv.org/abs/2501.10841 · HTML: https://mlsanity.com/paper/2501.10841/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 18 Jan 2025.

## Builds on (most cited first, 1 of 1)

- [Measuring Re-identification Risk](https://mlsanity.com/paper/2304.07210/) (2023)

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

- [ContextLeak: Auditing Leakage in Private In-Context Learning Methods](https://mlsanity.com/paper/2512.16059/) (2025)
- [Contextual trajectory and incremental contextual displacement: Towards using LLMs to understand dynamic, utterance-specific meaning construction](https://mlsanity.com/paper/2610.00840/) (2026)

## Abstract (arXiv)

To prove that a dataset is sufficiently anonymized, many privacy policies suggest that a re-identification risk assessment be performed, but do not provide a precise methodology for doing so, leaving the industry alone with the problem. This paper proposes a practical and ready-to-use methodology for re-identification risk assessment, the originality of which is manifold: (1) it is the first to follow well-known risk analysis methods (e.g. EBIOS) that have been used in the cybersecurity field for years, which consider not only the ability to perform an attack, but also the impact such an attack can have on an individual; (2) it is the first to qualify attributes and values of attributes with e.g. degree of exposure, as known real-world attacks mainly target certain types of attributes and not others.