Jeremy Seeman's Page

This page is only periodically updated - see my Google Scholar page for more complete publication updates.

Selected publications

  • Spector-Bagdady, Seeman, Marcus, and Maust. “Identifying Identifiability: From Regulatory Illusions to Techno-normative Frameworks to Protect Health Data Privacy.” 2026, Privacy Law Scholar’s Conference, Full Paper Under Revision. preprint.
  • Seeman, Si, and Reiter. “Toward Differentially Private Finite Population Estimation: An Approach Based on Survey Weight Regularization.” 2026, Harvard Data Science Review article.
  • Abdu, Seeman, and Jacobs. “Ethical Closure Problems in Formalizing Technologies.” 2026, Privacy Law Scholar’s Conference, Full Paper Under Revision at ACM Journal on Responsible Computing. preprint
  • Neunhoffer, Seeman, and Drechsler. “On the Formal Privacy Guarantees of Synthetic Data (Generated Without Formal Privacy Guarantees).” 2026, Harvard Data Science Review. article
  • Seeman, Si, and Reiter. “Differentially Private Population Quantity Estimates via Survey Weight Regularization.” 2025, National Bureau of Economic Research preprint.
  • Seeman and Susser. “Critical Provocations for Synthetic Data.” Surveillance and Society, 2024. preprint.
  • Seeman, Sexton, Pujol, and Machanavajjhala. “Privately Answering Queries on Skewed Data via Per Record Differential Privacy.” Very Large DataBases, 2024. preprintposter
  • Seeman and Susser. “Between Privacy and Utility: On Differential Privacy in Theory and Practice.” ACM Journal on Responsible Computing, 2023. preprint.
  • Seeman. “Bettery Privacy Theorists for Better Data Stewards.” Journal of Privacy and Confidentiality, 2023. preprint
  • Seeman. “Private Treatment Assignment for Causal Experiments.” Theory and Practice of Differential Privacy, 2023. poster
  • Seeman. “Framing Effects in the Operationalization of Differential Privacy Systems as Code-Driven Law.” International Conference on Computer Ethics: Philosophical Enquiry, 2023. article
  • Seeman, Reimherr, and Slavkovic. “Formal Privacy for Partially Private Data.” Under revision at Journal of Machine Learning Research, 2022. preprint
  • Seeman, Reimherr, and Slavkovic. “Exact Privacy Guarantees for Sampling Algorithms Implementing the Exponential Mechanism.” Advances in Neural Information Processing Systems, 2021. article

Selected policy briefs

  • Seeman, Williams, and Bowen. “Synthetic Data for Nebraska’s Statewide Workforce and Education Reporting System (NSWERS)”. Urban Institute. article.
  • Cummings, Hod, Jain, Kaptchuk, Mukherjee, Nanayakkara, Sarathy, and Seeman. “Increasing Responsible Data Sharing Capacity throughout Government.” Federation of American Scientists. article

Selected talks

  • Seeman. “Interfacing Statistics and DP: Method and Mess.” Keynote, International Conference on Theory and Practice of Differential Privacy (TPDP), Boston, MA, 2023. slides
  • Seeman. “Misspecification and Uncertainty Quantification in Differential Privacy.” Invited Talk, Fields Institute Workshop on Differential Privacy and Statistical Data Analysis, Fields Institute, Toronto, ON, 2023. video.