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Statistical anonymity: Quantifying reidentification risks without reidentifying users. (arXiv:2201.12306v1 [cs.DS])
Jan. 31, 2022, 2:20 a.m. | Gecia Bravo-Hermsdorff, Robert Busa-Fekete, Lee M. Gunderson, Andrés Munõz Medina, Umar Syed
cs.CR updates on arXiv.org arxiv.org
Data anonymization is an approach to privacy-preserving data release aimed at
preventing participants reidentification, and it is an important alternative to
differential privacy in applications that cannot tolerate noisy data. Existing
algorithms for enforcing $k$-anonymity in the released data assume that the
curator performing the anonymization has complete access to the original data.
Reasons for limiting this access range from undesirability to complete
infeasibility. This paper explores ideas -- objectives, metrics, protocols, and
extensions -- for reducing the trust that …
More from arxiv.org / cs.CR updates on arXiv.org
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