Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Drechsler, Joerg"'
Autor:
Drechsler, Jörg, Bailie, James
The concept of differential privacy (DP) has gained substantial attention in recent years, most notably since the U.S. Census Bureau announced the adoption of the concept for its 2020 Decennial Census. However, despite its attractive theoretical prop
Externí odkaz:
http://arxiv.org/abs/2408.07006
The idea to generate synthetic data as a tool for broadening access to sensitive microdata has been proposed for the first time three decades ago. While first applications of the idea emerged around the turn of the century, the approach really gained
Externí odkaz:
http://arxiv.org/abs/2304.02107
Differential privacy is a restriction on data processing algorithms that provides strong confidentiality guarantees for individual records in the data. However, research on proper statistical inference, that is, research on properly quantifying the u
Externí odkaz:
http://arxiv.org/abs/2106.10333
Recent research in differential privacy demonstrated that (sub)sampling can amplify the level of protection. For example, for $\epsilon$-differential privacy and simple random sampling with sampling rate $r$, the actual privacy guarantee is approxima
Externí odkaz:
http://arxiv.org/abs/2103.09705
Autor:
Drechsler, Joerg
Publikováno v:
Journal of the American Statistical Association, 2023 (online first)
Government agencies typically need to take potential risks of disclosure into account whenever they publish statistics based on their data or give external researchers access to collected data. In this context, the promise of formal privacy guarantee
Externí odkaz:
http://arxiv.org/abs/2102.08847
Sampling schemes are fundamental tools in statistics, survey design, and algorithm design. A fundamental result in differential privacy is that a differentially private mechanism run on a simple random sample of a population provides stronger privacy
Externí odkaz:
http://arxiv.org/abs/2007.12674
Data on businesses collected by statistical agencies are challenging to protect. Many businesses have unique characteristics, and distributions of employment, sales, and profits are highly skewed. Attackers wishing to conduct identification attacks o
Externí odkaz:
http://arxiv.org/abs/2008.02246
Autor:
Drechsler, Joerg, Hu, Jingchen
We investigate whether generating synthetic data can be a viable strategy for providing access to detailed geocoding information for external researchers, without compromising the confidentiality of the units included in the database. Our work was mo
Externí odkaz:
http://arxiv.org/abs/1803.05874
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Drechsler, Jörg, Shlomo, Natalie
Publikováno v:
Journal of the Royal Statistical Society. Series A (Statistics in Society), 2018 Jan 01. 181(3), 607-608.
Externí odkaz:
https://www.jstor.org/stable/48547505
