دورية أكاديمية
INSPECTRE: Privately Estimating the Unseen
| العنوان: | INSPECTRE: Privately Estimating the Unseen |
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| المؤلفون: | Jayadev Acharya, Gautam Kamath, Ziteng Sun, Huanyu Zhang |
| المصدر: | The Journal of Privacy and Confidentiality, Vol 10, Iss 2 (2020) |
| بيانات النشر: | Labor Dynamics Institute, 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Technology LCC:Social Sciences |
| مصطلحات موضوعية: | differential privacy, statistics, property estimation, Technology, Social Sciences |
| الوصف: | We develop differentially private methods for estimating various distributional properties. Given a sample from a discrete distribution p, some functional f, and accuracy and privacy parameters alpha and epsilon, the goal is to estimate f(p) up to accuracy alpha, while maintaining epsilon-differential privacy of the sample. We prove almost-tight bounds on the sample size required for this problem for several functionals of interest, including support size, support coverage, and entropy. We show that the cost of privacy is negligible in a variety of settings, both theoretically and experimentally. Our methods are based on a sensitivity analysis of several state-of-the-art methods for estimating these properties with sublinear sample complexities |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2575-8527 |
| Relation: | https://journalprivacyconfidentiality.org/index.php/jpc/article/view/724; https://doaj.org/toc/2575-8527 |
| DOI: | 10.29012/jpc.724 |
| URL الوصول: | https://doaj.org/article/8423f407cda248c9a041eb9b0996ce0e |
| رقم الأكسشن: | edsdoj.8423f407cda248c9a041eb9b0996ce0e |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 25758527 |
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| DOI: | 10.29012/jpc.724 |