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.

Favaro, S., Panero, F., Rigon, T. (2021). Bayesian nonparametric disclosure risk assessment. ELECTRONIC JOURNAL OF STATISTICS, 15(2), 5626-5651 [10.1214/21-EJS1933].

Bayesian nonparametric disclosure risk assessment

Rigon, Tommaso
2021

Abstract

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.
Articolo in rivista - Articolo scientifico
Bayesian nonparametrics; data confidentiality; Dirichlet process prior; disclosure risk assessment; empirical Bayes; Pitman– Yor process prior;
English
2021
15
2
5626
5651
open
Favaro, S., Panero, F., Rigon, T. (2021). Bayesian nonparametric disclosure risk assessment. ELECTRONIC JOURNAL OF STATISTICS, 15(2), 5626-5651 [10.1214/21-EJS1933].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/342516
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