Adam Sealfon
I am a research scientist in the Algorithms team at Google Research. I am currently working on problems related to differential privacy. I am also broadly interested in algorithms, cryptography, and machine learning, particularly provable guarantees for data privacy, robustness and security.
I received a PhD in computer science from MIT, where I was advised by Shafi Goldwasser and was a member of the Theory of Computation (TOC) and Cryptography and Information Security (CIS) groups. I also have an AB in mathematics and an SM in computer science from Harvard University, where I was advised by Salil Vadhan. Before joining Google Research, I was a postdoc at UC Berkeley working with Jacob Steinhardt and Michael Jordan and affiliated with the Berkeley Artificial Intelligence Research (BAIR) group.
Publications
See also DBLP
and Google Scholar.
Authors are ordered alphabetically by last name.
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URANIA: Differentially Private Insights into AI Use
Daogao Liu, Edith Cohen, Badih Ghazi, Peter Kairouz, Pritish Kamath, Alexander Knop, Ravi Kumar, Pasin Manurangsi, Adam Sealfon, Da Yu, and Chiyuan Zhang
Conference on Language Modeling (COLM), 2025
Available at [arXiv] -
Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, and Adam Sealfon
International Conference on Machine Learning (ICML), 2024
Available at [arXiv] -
Summary Reports Optimization in the Privacy Sandbox Attribution Reporting API
Hidayet Aksu, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon, and Avinash V. Varadarajan
Proceedings on Privacy Enhancing Technologies Symposium (PoPETS), 2024
Available at [arXiv] -
Scan, Shuffle, Rescan: Machine-Assisted Election Audits With Untrusted Scanners
Douglas W. Jones, Sunoo Park, Ronald L. Rivest, and Adam Sealfon
Financial Cryptography and Data Security (FC), 2024
Available at [ePrint] -
On Computing Pairwise Statistics with Local Differential Privacy
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, and Adam Sealfon
Neural Information Processing Systems (NeurIPS), 2023
Available at [arXiv] -
Synchronizable Fair Exchange
Ranjit Kumaresan, Srinivasan Raghuraman, and Adam Sealfon
Theory of Cryptography Conference (TCC), 2023
Available at [ePrint] -
Optimizing Hierarchical Queries for the Attribution Reporting API
Matt Dawson, Badih Ghazi, Pritish Kamath, Kapil Kumar, Ravi Kumar, Bo Luan, Pasin Manurangsi, Nishanth Mundru, Harikesh Nair, Adam Sealfon, and Shengyu Zhu
AdKDD Workshop at Knowledge Discovery and Data Mining (KDD), 2023
Available at [arXiv] -
Batch Verification for Statistical Zero Knowledge Proofs
Inbar Kaslasi, Guy N. Rothblum, Ron D. Rothblum, Adam Sealfon, and Prashant Nalini Vasudevan
Theory of Cryptography Conference (TCC), 2020
Available at [ePrint, ECCC] -
Efficiently Estimating Erdős-Rényi Graphs with Node Differential Privacy
Adam Sealfon and Jon Ullman
Neural Information Processing Systems (NeurIPS), 2019
Invited to Journal of Privacy and Confidentiality, 2021
Available at [arXiv] -
It wasn't me! Repudiability and Claimability of Ring Signatures
Sunoo Park and Adam Sealfon
International Cryptology Conference (CRYPTO), 2019
Available at [ePrint] -
Towards Non-Interactive Zero-Knowledge for NP from LWE
Ron D. Rothblum, Adam Sealfon, and Katerina Sotiraki
Public Key Cryptography (PKC), 2019
Invited to Journal of Cryptology, 2021
Available at [ePrint] -
Population Stability: Regulating Size in the Presence of an Adversary
Shafi Goldwasser, Rafail Ostrovsky, Alessandra Scafuro, and Adam Sealfon
Principles of Distributed Computing (PODC), 2018
Available at [arXiv] -
Network Oblivious Transfer
Ranjit Kumaresan, Srinivasan Raghuraman, and Adam Sealfon
International Cryptology Conference (CRYPTO), 2016
Available at [ePrint] -
Shortest Paths and Distances with Differential Privacy
Adam Sealfon
Principles of Database Systems (PODS), 2016
Recipient of best student paper award
Available at [arXiv] -
Truth Tellers and Liars with Fewer Questions
Gilad Braunschvig, Alon Brutzkus, David Peleg, and Adam Sealfon
Discrete Mathematics, 2015
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Fault Tolerant Additive and (μ, α)-Spanners
Gilad Braunschvig, Shiri Chechik, David Peleg, and Adam Sealfon
Theoretical Computer Science, 2015
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Agreement in Partitioned Dynamic Networks
Adam Sealfon and Katerina Sotiraki
Brief announcement, Distributed Computing (DISC), 2014
Available at [arXiv]
Theses
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Keep it secret, keep it safe: privacy, security, and robustness in an adversarial world
Adam Sealfon
PhD thesis, Massachusetts Institute of Technology
Available at [MIT Libraries] -
Fault tolerant graph spanners
Adam Sealfon
Undergraduate honors thesis, Harvard University