Joe Near

Assistant Professor
University of Vermont
Office: E458 Innovation Hall
E-mail: jnear at uvm dot edu
[ CV ]

[ Books | Teaching | Students | Service | Publications ]

Research Interests

My research interests include data privacy (especially differential privacy), security (especially secure/verified computation), fairness, programming languages, and machine learning.


Programming Differential Privacy
A book about differential privacy, for programmers.
By Joseph P. Near and Chiké Abuah

Differential Privacy for Databases
By Joseph P. Near and Xi He


Graduate Students (advised or co-advised)



Program Committees

  • 2024: AISTATS
  • 2023: CCS, VLDB, TPDP
  • 2021: TPDP
  • 2020: TPDP, FCS


Recent Publications

  • Solo: A Lightweight Static Analysis for Differential Privacy
    Chike Abuah, David Darais, Joseph P. Near
    In OOPSLA, 2022
  • Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors
    Timothy Stevens, Christian Skalka, Christelle Vincent, John Ring, Samuel Clark, Joseph P. Near
    In USENIX Security, 2022
  • PrivGuard: Privacy Regulation Compliance Made Easier
    Lun Wang, Usmann Khan, Joseph P. Near, Qi Pang, Jithendaraa Subramanian, Neel Somani, Peng Gao, Andrew Low, and Dawn Song
    In USENIX Security, 2022
  • Zero Knowledge Static Program Analysis
    Zhiyong Fang, David Darais, Joseph P. Near, and Yupeng Zhang
    In Communications and Computer Security (CCS), 2021
  • DDUO: General-Purpose Dynamic Analysis for Differential Privacy
    Chike Abuah, Alex Silence, David Darais, Joseph P. Near
    In Computer Security Foundations (CSF), 2021
  • Chorus: a Programming Framework for Building Scalable Differential Privacy Mechanisms
    Noah Johnson, Joseph P. Near, Joseph M. Hellerstein, and Dawn Song
    In European Symposium on Security & Privacy (EuroS&P), 2020

E458 Innovation Hall, Burlington, VT