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Bayesian nonparametric disclosure risk assessment

Favaro, Stefano, Panero, Francesca and Rigon, Tommaso (2021) Bayesian nonparametric disclosure risk assessment. Electronic Journal of Statistics, 15 (2). 5626 - 5651. ISSN 1935-7524

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Identification Number: 10.1214/21-EJS1933


Any decision about the release of microdata for public use is supported by the estimation of measures of disclosure risk, the most popular being the number τ1 of sample uniques that are also population uniques. In such a context, parametric and nonparametric partition-based models have been shown to have: i) the strength of leading to estimators of τ1 with desirable features, including ease of implementation, computational efficiency and scalability to massive data; ii) the weakness of producing underestimates of τ1 in realistic scenarios, with the underestimation getting worse as the tail behaviour of the empirical distribution of microdata gets heavier. To fix this underestimation phenomenon, we propose a Bayesian nonparametric partition-based model that can be tuned to the tail behaviour of the empirical distribution of microdata. Our model relies on the Pitman–Yor process prior, and it leads to a novel estimator of τ1 with all the desirable features of partition-based estimators and that, in addition, allows to reduce underestimation by tuning a “discount” parameter. We show the effectiveness of our estimator through its application to synthetic data and real data.

Item Type: Article
Official URL:
Additional Information: © 2022 The Authors
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 11 Nov 2022 17:30
Last Modified: 21 Feb 2023 17:27

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