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239 results
2024
Shurong Lin, Mark Bun, Marco Gaboardi, Eric D. Kolaczyk, and Adam Smith. 2024. “Differentially Private Confidence Intervals for Proportions under Stratified Random Sampling”. Electronic Journal of Statistics, 18, 1, Pp. 1455-94
Shurong Lin, Mark Bun, Marco Gaboardi, Eric D. Kolaczyk, and Adam Smith. 2024. “Differentially Private Confidence Intervals for Proportions under Stratified Random Sampling”. Electronic Journal of Statistics, 18, 1, Pp. 1455-94
Confidence intervals are a fundamental tool for quantifying the uncertainty of parameters of interest. With the increase of data privacy awareness, developing a private version of confidence intervals has gained growing attention from both statisticians...
Ethan Cowan, Michael Shoemate, and Mayana Pereira. 2024. Hands-On Differential Privacy. O’Reilly Media
Ethan Cowan, Michael Shoemate, and Mayana Pereira. 2024. Hands-On Differential Privacy. O’Reilly Media
Many organizations today analyze and share large, sensitive datasets about individuals. Whether these datasets cover healthcare details, financial records, or exam scores, it's become more difficult for organizations to protect an individual's...
Nico Manzanelli, Wanrong Zhang, and Salil Vadhan. 2024. “Membership Inference Attacks and Privacy in Topic Modeling”. Accepted at Transactions on Machine Learning Research (TMLR) 2024
Nico Manzanelli, Wanrong Zhang, and Salil Vadhan. 2024. “Membership Inference Attacks and Privacy in Topic Modeling”. Accepted at Transactions on Machine Learning Research (TMLR) 2024
Recent research shows that large language models are susceptible to privacy attacks that infer aspects of the training data. However, it is unclear if simpler generative models, like topic models, share similar vulnerabilities. In this work, we propose an...
Jack Fitzsimons, James Honaker, Michael Shoemate, and Vikrant Singhal. 2024. “Private Means and the Curious Incident of the Free Lunch”. Accepted As a Poster at the Theory and Practice of Differential Privacy (TPDP) 2024
Jack Fitzsimons, James Honaker, Michael Shoemate, and Vikrant Singhal. 2024. “Private Means and the Curious Incident of the Free Lunch”. Accepted As a Poster at the Theory and Practice of Differential Privacy (TPDP) 2024
James Bailie and Jörg Drechsler. 2024. “Whose Data Is It Anyway? Towards a Formal Treatment of Differential Privacy for Surveys”
James Bailie and Jörg Drechsler. 2024. “Whose Data Is It Anyway? Towards a Formal Treatment of Differential Privacy for Surveys”
This paper develops theory for understanding and implementing differential privacy in the context of survey statistics. By recognizing the major phases in the survey-data pipeline, we identified ten different settings of DP. These settings correspond to...
Shurong Lin, Elliot Paquette, and Eric D. Kolaczyk. 2024. “Differentially Private Linear Regression With Linked Data”. ArXiv Preprint
Shurong Lin, Elliot Paquette, and Eric D. Kolaczyk. 2024. “Differentially Private Linear Regression With Linked Data”. ArXiv Preprint
There has been increasing demand for establishing privacy-preserving methodologies for modern statistics and machine learning. Differential privacy, a mathematical notion from computer science, is a rising tool offering robust privacy guarantees. Recent...
Jörg Drechsler and James Bailie. 2024. “The Complexities of Differential Privacy for Survey Data”. Will Appear in the Edited NBER Volume: “Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and Their Consequences”
Jörg Drechsler and James Bailie. 2024. “The Complexities of Differential Privacy for Survey Data”. Will Appear in the Edited NBER Volume: “Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and Their Consequences”
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...
Marco Gaboardi, Michael Hay, and Salil Vadhan. 2024. “Programming Frameworks for Differential Privacy”. In To Appear As a Chapter in the Book "Differential Privacy for Artificial Intelligence," Edited by Ferdinando Fioretto and Pascal Van Hentenryck
Marco Gaboardi, Michael Hay, and Salil Vadhan. 2024. “Programming Frameworks for Differential Privacy”. In To Appear As a Chapter in the Book "Differential Privacy for Artificial Intelligence," Edited by Ferdinando Fioretto and Pascal Van Hentenryck
Many programming frameworks have been introduced to support the development of differentially private software applications. In this chapter, we survey some of the conceptual ideas underlying these frameworks in a way that we hope will be helpful for both...
Palak Jain, Adam Smith, and Connor Wagaman. 2024. “Time-Aware Projections: Truly Node-Private Graph Statistics under Continual Observation”
Palak Jain, Adam Smith, and Connor Wagaman. 2024. “Time-Aware Projections: Truly Node-Private Graph Statistics under Continual Observation”
We describe the first algorithms that satisfy the standard notion of node-differential privacy in the continual release setting (i.e., without an assumed promise on input streams). Previous work addresses node-private continual release by assuming an...
Mark Bun, Marco Gaboardi, Marcel Neunhoffer, and Wanrong Zhang. 2024. “Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections”
Mark Bun, Marco Gaboardi, Marcel Neunhoffer, and Wanrong Zhang. 2024. “Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections”
Motivated by privacy concerns in long-term longitudinal studies in medical and social science research, we study the problem of continually releasing differentially private synthetic data from longitudinal data collections. We introduce a model where, in...
Rachel Cummings and Jayshree Sarathy. 2024. “Centering Policy and Practice: Research Gaps Around Usable Differential Privacy”. In 2023 5th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)
Rachel Cummings and Jayshree Sarathy. 2024. “Centering Policy and Practice: Research Gaps Around Usable Differential Privacy”. In 2023 5th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)
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 clean...
2023
Raphaël Fondeville, Michael Shoemate, Wanrong Zhang, and Salil Vadhan. 2023. “Protecting High-Resolution Poverty Statistics Against Disclosure Using Differential Privacy”. In UNECE Expert Meeting on Statistical Data Confidentiality 2023
Raphaël Fondeville, Michael Shoemate, Wanrong Zhang, and Salil Vadhan. 2023. “Protecting High-Resolution Poverty Statistics Against Disclosure Using Differential Privacy”. In UNECE Expert Meeting on Statistical Data Confidentiality 2023
The past few years have seen an explosion of the volume of geo-referenced data, a trend that can be observed in the world of official statistics: large scale imputation, generalizing survey results to the whole population, is made more and more common...