Joe Near

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

[ Books | Teaching | Students | Service | Publications ]

• Member, UVM PLAID lab
• Member, UVM Center for Computer Security and Privacy

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

• CS 202: Compiler Construction (Spring 2022, Spring 2021, Spring 2020, Spring 2019)
• CS 211: Data Privacy (Fall 2021, Fall 2020, Fall 2019, Fall 2018)
• CS 295: Secure Distributed Computation (Fall 2020)

• Chike Abuah
• Craig Agricola
• Krystal Maughan
• Ivoline Ngong
• Tim Stevens

Organization

• FCS: Foundations of Computer Security Workshop (2022, 2021)

Program Committees

• 2022: CCS, AISTATS, VLDB
• 2021: TPDP
• 2020: TPDP, FCS

• Backpropagation Clipping for Deep Learning with Differential Privacy
  Timothy Stevens, Ivoline C. Ngong, David Darais, Calvin Hirsch, David Slater, Joseph P. Near
  
• Prediction Sensitivity: Continual Audit of Counterfactual Fairness in Deployed Classifiers
  Krystal Maughan, Ivoline C. Ngong, Joseph P. Near
  
• Secret Sharing Sharing For Highly Scalable Secure Aggregation
  Timothy Stevens, Joseph P. Near, Christian Skalka
  
• Do I Get the Privacy I Need? Benchmarking Utility in Differential Privacy Libraries
  Gonzalo Munilla Garrido, Joseph P. Near, Aitsam Muhammad, Warren He, Roman Matzutt, Florian Matthes
  
• Solo: A Lightweight Static Analysis for Differential Privacy
  Chike Abuah, David Darais, Joseph P. Near
  
• Contextual Linear Types for Differential Privacy
  Matias Toro, David Darais, Chike Abuah, Joe Near, Damian Arquez, Federico Olmedo, Eric Tanter
  

Recent Publications

• 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
  (PDF)
• PrivGuard: Privacy Regulation Compliance Made Easier
  Lun Wang, Usmann Khan, Joseph P. Near, Zhejiang University, 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
  (PDF)
• 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
  (PDF)
• Duet: An Expressive Higher-Order Language and Linear Type System for Statically Enforcing Differential Privacy
  Joseph P. Near, David Darais, Chike Abuah, Tim Stevens, Pranav Gaddamadugu, Lun Wang, Neel Somani, Mu Zhang, Nikhil Sharma, Alex Shan, and Dawn Song
  In Object-oriented Programming, Systems, Languages, and Applications (OOPSLA). 2019
  (PDF)