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.


See also DBLP and Google Scholar. Authors are ordered alphabetically by last name.
  • Scan, Shuffle, Rescan: Machine-Assisted Election Audits With Untrusted Scanners
    Douglas W. Jones, Sunoo Park, Ronald L. Rivest and Adam Sealfon
    To appear in 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
    To appear in Neural Information Processing Systems (NeurIPS) 2023
  • 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 2023, workshop at Knowledge Discovery and Data Mining (KDD)
    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]
  • 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
  • Fault Tolerant Additive and (μ, α)-Spanners
    Gilad Braunschvig, Shiri Chechik, David Peleg and Adam Sealfon
    Theoretical Computer Science, 2015
  • Agreement in Partitioned Dynamic Networks
    Adam Sealfon and Katerina Sotiraki
    Brief announcement, Distributed Computing (DISC) 2014
    Available at [arXiv]


  • Keep it secret, keep it safe: privacy, security, and robustness in an adversarial world
    Adam Sealfon
    PhD thesis
    Available at [MIT Libraries]
  • Fault tolerant graph spanners
    Adam Sealfon
    Undergraduate honors thesis