# Multi-Layer Privacy-Preserving Record Linkage with Clerical Review based on gradual information disclosure

arXiv: https://arxiv.org/abs/2412.04178 · HTML: https://mlsanity.com/paper/2412.04178/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 32 references that are in the map.
- First posted on arXiv 5 Dec 2024.

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

- [Increasing Transparent and Accountable Use of Data by Quantifying the Actual Privacy Risk in Interactive Record Linkage](https://mlsanity.com/paper/1906.03345/) (2019)

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

- [From Logic Monopoly to Social Contract: Separation of Power and the Institutional Foundations for Autonomous Agent Economies](https://mlsanity.com/paper/2603.25100/) (2026)

## Abstract (arXiv)

Privacy-Preserving Record linkage (PPRL) is an essential component in data integration tasks of sensitive information. The linkage quality determines the usability of combined datasets and (machine learning) applications based on them. We present a novel privacy-preserving protocol that integrates clerical review in PPRL using a multi-layer active learning process. Uncertain match candidates are reviewed on several layers by human and non-human oracles to reduce the amount of disclosed information per record and in total. Predictions are propagated back to update previous layers, resulting in an improved linkage performance for non-reviewed candidates as well. The data owners remain in control of the amount of information they share for each record. Therefore, our approach follows need-to-know and data sovereignty principles. The experimental evaluation on real-world datasets shows considerable linkage quality improvements with limited labeling effort and privacy risks.