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239 results
2023
Daniel G. Alabi and Salil P. Vadhan. 2023. “Differentially Private Hypothesis Testing for Linear Regression”. Journal of Machine Learning Research, 24, 1-50
Daniel G. Alabi and Salil P. Vadhan. 2023. “Differentially Private Hypothesis Testing for Linear Regression”. Journal of Machine Learning Research, 24, 1-50
In this work, we design differentially private hypothesis tests for the following problems in the multivariate linear regression model: testing a linear relationship and testing for the presence of mixtures. The majority of our hypothesis tests are based...
Samuel Haney, Michael Shoemate, Grace Tian, Salil Vadhan, Andrew Vyrros, Vicki Xu, and Wanrong Zhang. 2023. “Concurrent Composition for Interactive Differential Privacy With Adaptive Privacy-Loss Parameters”
Samuel Haney, Michael Shoemate, Grace Tian, Salil Vadhan, Andrew Vyrros, Vicki Xu, and Wanrong Zhang. 2023. “Concurrent Composition for Interactive Differential Privacy With Adaptive Privacy-Loss Parameters”
In this paper, we study the concurrent composition of interactive mechanisms with adaptively chosen privacy-loss parameters. In this setting, the adversary can interleave queries to existing interactive mechanisms, as well as create new ones. We prove...
2022
Daniel Alabi, Adam Smith, Audra McMillan, Salil Vadhan, and Jayshree Sarathy. 2022. “Differentially Private Simple Linear Regression”. ArXiv:2007.05157
Daniel Alabi, Adam Smith, Audra McMillan, Salil Vadhan, and Jayshree Sarathy. 2022. “Differentially Private Simple Linear Regression”. ArXiv:2007.05157
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 individual...
Rachel Cummings, Yajun Mei, and Wanrong Zhang. 2022. “Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size.”. In In The 25th International Conference on Artificial Intelligence and Statistics (AISTATS)
Rachel Cummings, Yajun Mei, and Wanrong Zhang. 2022. “Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size.”. In In The 25th International Conference on Artificial Intelligence and Statistics (AISTATS)
The sequential hypothesis testing problem is a class of statistical analyses where the sample size is not fixed in advance, and the analyst must make real-time decisions until a stopping criterion is reached. In this work, we study the sequential...
Daniel Alabi, Badih Ghazi, Ravi Kumar, and Pasin Manurangsi. 2022. “Private Rank Aggregation in Central and Local Models.”. In In Proceedings of the 2022 AAAI Conference on Artificial Intelligence
Daniel Alabi, Badih Ghazi, Ravi Kumar, and Pasin Manurangsi. 2022. “Private Rank Aggregation in Central and Local Models.”. In In Proceedings of the 2022 AAAI Conference on Artificial Intelligence
In social choice theory, (Kemeny) rank aggregation is a well-studied problem where the goal is to combine rankings from multiple voters into a single ranking on the same set of items. Since rankings can reveal preferences of voters (which a voter might...
Mark Bun, Jörg Drechsler, Marco Gaboardi, Audra McMillan, and Jayshree Sarathy. 2022. “Controlling Privacy Loss in Sampling Schemes: An Analysis of Stratified and Cluster Sampling.”. In In Foundations of Responsible Computing (FORC 2022)
Mark Bun, Jörg Drechsler, Marco Gaboardi, Audra McMillan, and Jayshree Sarathy. 2022. “Controlling Privacy Loss in Sampling Schemes: An Analysis of Stratified and Cluster Sampling.”. In In Foundations of Responsible Computing (FORC 2022)
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...
Jörg Drechsler, Ira Globus-Harris, Audra McMillan, Jayshree Sarathy, and Adam Smith. 2022. “Nonparametric Differentially Private Confidence Intervals for the Median.”. To Appear in the Journal of Survey Statistics and Methodology (JSSAM)
Jörg Drechsler, Ira Globus-Harris, Audra McMillan, Jayshree Sarathy, and Adam Smith. 2022. “Nonparametric Differentially Private Confidence Intervals for the Median.”. To Appear in the Journal of Survey Statistics and Methodology (JSSAM)
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...
boyd and Jayshree Sarathy. 2022. “Differential Perspectives: Epistemic Disconnects Surrounding the US Census Bureau’s Use of Differential Privacy”. To Appear in the Harvard Data Science Review (HDSR)
boyd and Jayshree Sarathy. 2022. “Differential Perspectives: Epistemic Disconnects Surrounding the US Census Bureau’s Use of Differential Privacy”. To Appear in the Harvard Data Science Review (HDSR)
When the U.S. Census Bureau announced its intention to modernize its disclosure
avoidance procedures for the 2020 Census, it sparked a controversy that is still underway. The move to differential privacy introduced technical and procedural uncertainties...
avoidance procedures for the 2020 Census, it sparked a controversy that is still underway. The move to differential privacy introduced technical and procedural uncertainties...
Jayshree Sarathy. 2022. From Algorithmic to Institutional Logics: The Politics of Differential Privacy
Jayshree Sarathy. 2022. From Algorithmic to Institutional Logics: The Politics of Differential Privacy
Over the past two decades, we have come to see that traditional de-anonymization techniques fail to protect the privacy of individuals in sensitive datasets. To address this problem, computer scientists introduced differential privacy, a strong...
Sílvia Casacuberta, Michael Shoemate, Salil Vadhan, and Connor Wagaman. 2022. “Widespread Underestimation of Sensitivity in Differentially Private Libraries and How to Fix It”. In Theory and Practice of Differential Privacy 2022
Sílvia Casacuberta, Michael Shoemate, Salil Vadhan, and Connor Wagaman. 2022. “Widespread Underestimation of Sensitivity in Differentially Private Libraries and How to Fix It”. In Theory and Practice of Differential Privacy 2022
We identify a new class of vulnerabilities in implementations of differential privacy. Specifically, they arise when computing basic statistics such as sums, thanks to discrepancies between the implemented arithmetic using finite data types (namely, ints...
Daniel Alabi and Salil Vadhan. 2022. “Hypothesis Testing for Differentially Private Linear Regression”. In Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS ‘22)
Daniel Alabi and Salil Vadhan. 2022. “Hypothesis Testing for Differentially Private Linear Regression”. In Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS ‘22)
In this work, we design differentially private hypothesis tests for the following problems in the general linear model: testing a linear relationship and testing for the presence of mixtures. The majority of our hypothesis tests are based on...
Ruobin Gong, Erica L. Groshen, and Salil Vadhan. 2022. “Harnessing the Known Unknowns: Differential Privacy and the 2020 Census (co-Editors’ Forward)”. Harvard Data Science Review, Special Issue 2
Ruobin Gong, Erica L. Groshen, and Salil Vadhan. 2022. “Harnessing the Known Unknowns: Differential Privacy and the 2020 Census (co-Editors’ Forward)”. Harvard Data Science Review, Special Issue 2