Research › Secure Computation

Secure and Distributed Computation

Secure multiparty computation lets several parties compute a joint result while each keeps its own data secret. We build protocols for differentially private secure aggregation in federated learning, simulation frameworks for measuring their real-world scalability, and choreographic programming languages that make distributed protocols easier to write and to prove correct. This work is supported by an NSF CAREER award and by DARPA.

Faculty

Christian Skalka Director

Professor and Chair, Department of Computer Science

Joseph Near

Associate Professor, Department of Computer Science

Christelle Vincent

Associate Professor, Department of Mathematics and Statistics

Selected publications

arXiv 2026

Probabilistic, Resource-Aware, Asynchronous, Out-of-Order Choreographies

Mako Bates, Steven Baldasty, Ernest Hyun, Christian Skalka, Joseph Near

arXiv (preprint)
arXiv 2026

Qoreo: Choreographic Programming for Quantum Distributed Systems

Jennifer Paykin, Steven Baldasty, Joseph Near, Christian Skalka

arXiv (preprint)
PLDI 2025

Efficient, Portable, Census-Polymorphic Choreographic Programming

Mako Bates, Shun Kashiwa, Syed Jafri, Gan Shen, Lindsey Kuper, Joseph Near

ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI)
ESOP 2025

SMT-Boosted Security Types for Low-Level MPC

Christian Skalka, Joseph Near

European Symposium on Programming (ESOP), pp. 258–285
Book 2025

Programming MPC

Joseph Near

Book
PPDP 2024

Language-Based Security for Low-Level MPC

Christian Skalka, Joseph Near

International Symposium on Principles and Practice of Declarative Programming (PPDP)
SaTML 2024

Olympia: A Simulation Framework for Evaluating the Concrete Scalability of Secure Aggregation Protocols

Ivoline Ngong, Nicholas Gibson, Joseph Near

IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)
arXiv 2024

MultiChor: Census Polymorphic Choreographic Programming with Multiply Located Values

Mako Bates, Syed Jafri, Joseph Near

arXiv (preprint)
USENIX Security 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 Near

31st USENIX Security Symposium, pp. 1379–1395
PLAS 2022

PiMPC: Automatic Security Proofs for MPC Protocols

Mako Bates, Joseph Near

Workshop on Programming Languages and Analysis for Security (PLAS)

All publications in this area →

Related courses

Course	Description	Instructor
CS 3120 Secure Distributed Computation Secure Computation Cryptography	Computing on data that no single party is allowed to see: secure multiparty computation, homomorphic encryption, zero-knowledge proofs, blockchains, and encrypted databases. Prerequisites: CS 2240, CS 2250	Joseph Near
CS 5120 Advanced Secure Distributed Computation Secure Computation Cryptography	Graduate treatment of secure multiparty computation, homomorphic encryption, and zero-knowledge proofs, taught alongside CS 3120 with additional graduate-level work.	Joseph Near

All security and privacy courses →

Funded projects

NSF

CAREER: Distributed Differential Privacy via Secure Multiparty Computation

CAREER (Secure and Trustworthy Cyberspace) · CNS-2238442 · 2023–2028 · $548,929 · UVM PI: Joseph Near
DARPA

TOPSPIN: Tools for Proving Security and Performance of Instances of Weird Networks

Provably Weird Network Deployment and Detection (PWND2) · 2025–2027 · UVM PIs: Christian Skalka, Joseph Near
DARPA

Wizkit: Wide-Scale Zero-Knowledge Interpreter Toolkit

SIEVE · 2020–2024 · UVM PI: Joseph Near

Work with us

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