Harvard University Privacy Tools Project

logo-long.png

The Privacy Tools Project is a broad effort to advance a multidisciplinary understanding of data privacy issues and build computational, statistical, legal, and policy tools to help address these issues in a variety of contexts. It was incubated by Harvard's Center for Research on Computation and Society, and continues to be a collaborative effort between several units at Harvard University (the School of Engineering and Applied Sciences, Institute for Quantitative Social Science, and Berkman Klein Center for Internet & Society), Georgetown University (Computer Science Department), Boston University (Computer Science Department) and MIT (Center for Research in Equitable and Open Scholarship).

Our work is funded by the National Science Foundation, the Sloan Foundation, the US Bureau of the Census, and Google. Any opinions, findings, conclusions, or recommendations expressed on this website are those of the author(s) and do not necessarily reflect the views of our funders.

 

Featured Popular Articles

"Why The World Watches America's Lead On Privacy Issues"

by Adam Tanner (November 13, 2014 - Forbes)

Recent Publications

2024

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)
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
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
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
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