X^2R^2: a Tool for Explainable and Explorative Reidentification Risk Analysis
Summary: X²R² makes reidentification-risk analysis accessible through an explainable, interactive exploration workflow. It quantifies risks, recommends anonymization transformations, and exposes privacy–utility tradeoffs to nonexperts. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Tom Rolandus Hagedoorn (Eurecat)
- 2. Rohit Kumar (Eurecat)
- 3. Francesco Bonchi (Eurecat; ISI Foundation)
BibTeX Citation
@article{hagedoorn_vldb20,
title = {{X\^{}2R\^{}2: a Tool for Explainable and Explorative Reidentification Risk Analysis}},
author = {Hagedoorn, Tom Rolandus and Kumar, Rohit and Bonchi, Francesco},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {12},
pages = {2929--2932},
doi = {10.14778/3415478.3415511},
url = {https://doi.org/10.14778/3415478.3415511},
year = {2020}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 338 | Generalizing Data to Provide Anonymity when Disclosing Information | 1998 | PODS | 0.00020731054 |
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