Joe Near
He/him
Associate Professor
University of Vermont
Office: E458 Innovation Hall
E-mail: jnear at uvm dot edu
[ CV ]
Research Interests
My research interests include data privacy (especially differential privacy), security (especially secure/verified computation), fairness, programming languages, and machine learning.
Books
Teaching
Graduate Students (advised or co-advised)
Service
Organization
Program Committees
- 2024: CCS, AISTATS, PETS
- 2023: CCS, VLDB, TPDP
- 2022: CCS, AISTATS, VLDB, TPDP
- 2021: TPDP
- 2020: TPDP, FCS
Pre-prints
- MultiChor: Census Polymorphic Choreographic Programming with Multiply Located Values
Mako Bates, Syed Jafri, Joseph P. Near
- We Know I Know You Know; Choreographic Programming With Multicast and Multiply Located Values
Mako Bates, Joseph P. Near
- DT-SIM: Property-Based Testing for MPC Security
Mako Bates, Joseph P. Near
- Improving Utility for Privacy-Preserving Analysis of Correlated Columns using Pufferfish Privacy
Krystal Maughan, 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
Recent Publications
- Evaluating the Usability of Differential Privacy Tools with Data Practitioners
Ivoline C. Ngong, Brad Stenger, Joseph P. Near, Yuanyuan Feng
In SOUPS, 2024
(PDF)
- OLYMPIA: A Simulation Framework for Evaluating the Concrete Scalability of Secure Aggregation Protocols
Ivoline C. Ngong, Nicholas Gibson, Joseph P. Near
In IEEE SaTML, 2024
(PDF)
- Contextual Linear Types for Differential Privacy
Matias Toro, David Darais, Chike Abuah, Joseph P. Near, Damian Arquez, Federico Olmedo, Eric Tanter
ACM Transactions on Programming Languages and Systems, 2023
- Solo: A Lightweight Static Analysis for Differential Privacy
Chike Abuah, David Darais, Joseph P. Near
In OOPSLA, 2022
(PDF)
- 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, Qi Pang, Jithendaraa Subramanian, Neel Somani, Peng Gao, Andrew Low, and Dawn Song
In USENIX Security, 2022
E458 Innovation Hall,
Burlington, VT
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