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pro vyhledávání: '"Sarathy, Jayshree"'
Autor:
Cummings, Rachel, Sarathy, Jayshree
Publikováno v:
2023 5th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA). IEEE Computer Society, 2023
As a mathematically rigorous framework that has amassed a rich theoretical literature, differential privacy is considered by many experts to be the gold standard for privacy-preserving data analysis. Others argue that while differential privacy is a
Externí odkaz:
http://arxiv.org/abs/2406.12103
Differential Privacy (DP) is a mathematical framework that is increasingly deployed to mitigate privacy risks associated with machine learning and statistical analyses. Despite the growing adoption of DP, its technical privacy parameters do not lend
Externí odkaz:
http://arxiv.org/abs/2405.01716
Across academia, government, and industry, data stewards are facing increasing pressure to make datasets more openly accessible for researchers while also protecting the privacy of data subjects. Differential privacy (DP) is one promising way to offe
Externí odkaz:
http://arxiv.org/abs/2302.11775
Autor:
Sarathy, Jayshree, Vadhan, Salil
In this paper, we study differentially private point and confidence interval estimators for simple linear regression. Motivated by recent work that highlights the strong empirical performance of an algorithm based on robust statistics, DPTheilSen, we
Externí odkaz:
http://arxiv.org/abs/2207.13289
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
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
Economics and social science research often require analyzing datasets of sensitive personal information at fine granularity, with models fit to small subsets of the data. Unfortunately, such fine-grained analysis can easily reveal sensitive individu
Externí odkaz:
http://arxiv.org/abs/2007.05157
Publikováno v:
Proceedings on Privacy Enhancing Technologies. 2022:184-204
Economics and social science research often require analyzing datasets of sensitive personal information at fine granularity, with models fit to small subsets of the data. Unfortunately, such fine-grained analysis can easily reveal sensitive individu
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Akademický článek
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