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Chapitre D'ouvrage Année : 2023

Goodness-of-Fit Testing for Hölder Continuous Densities Under Local Differential Privacy

Résumé

We address the problem of goodness-of-fit testing for Hölder continuous densities under local differential privacy constraints. We study minimax separation rates when only non-interactive privacy mechanisms are allowed to be used and when both non-interactive and sequentially interactive can be used for privatisation. We propose privacy mechanisms and associated testing procedures whose analysis enables us to obtain upper bounds on the minimax rates. These results are complemented with lower bounds. By comparing these bounds, we show that the proposed privacy mechanisms and tests are optimal up to at most a logarithmic factor for several choices of including densities from uniform, normal, Beta, Cauchy, Pareto, exponential distributions. In particular, we observe that the results are deteriorated in the private setting compared to the non-private one. Moreover, we show that sequentially interactive mechanisms improve upon the results obtained when considering only non-interactive privacy mechanisms.

Dates et versions

hal-04426780 , version 1 (30-01-2024)

Identifiants

Citer

Amandine Dubois, Thomas Berrett, Cristina Butucea. Goodness-of-Fit Testing for Hölder Continuous Densities Under Local Differential Privacy. Foundations of Modern Statistics, PROMS-425, Springer International Publishing, pp.53-119, 2023, Springer Proceedings in Mathematics & Statistics, 978-3-031-30113-1. ⟨10.1007/978-3-031-30114-8_2⟩. ⟨hal-04426780⟩
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