Publications

Anonymous Rate-Limited Credentials

Published in IETF CFRG, 2025

Anonymous Rate-Limited Credentials (ARC) are a specialization of keyed-verification anonymous credentials with support for rate limiting. ARC credentials can be presented from client to server up to some fixed number of times, where each presentation is cryptographically bound to client secrets and application-specific public information, such that each presentation is unlinkable from the others as well as the original credential creation. ARC is useful in applications where a server needs to throttle or rate-limit access from anonymous clients.

Recommended citation: C. Yun and C. A. Wood, "Anonymous Rate-Limited Credentials", Work in Progress, Internet-Draft, draft-yun-cfrg-arc, 05 February 2025, <https://datatracker.ietf.org/doc/draft-yun-cfrg-arc/> https://datatracker.ietf.org/doc/draft-yun-cfrg-arc/

ACORN: Input Validation for Secure Aggregation

Published in Under submission, 2022

This paper presents ACORN, an secure aggregation extension that enables input validation to prevent malicious clients from gaining disproportionate influence on the computed aggregated statistics or machine learning model.

Recommended citation: James Bell, Adrià Gascón, Tancrède Lepoint, Baiyu Li, Sarah Meiklejohn, Mariana Raykova, Cathie Yun. (2022). "ACORN: Input Validation for Secure Aggregation." https://eprint.iacr.org/2022/1461

TxVM: A New Design for Blockchain Transactions

Published March 2018

With TxVM we seek to combine the respective strengths of the declarative and imperative approaches to representing blockchain transactions, while avoiding their weaknesses.

Recommended citation: Bob Glickstein, Cathie Yun, Dan Robinson, Keith Rarick, Oleg Andreev. (2018). "TxVM: A New Design for Blockchain Transactions.". https://chain.com/assets/txvm.pdf

Splinter: Practical Private Queries on Public Data

Published in Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI '17), Boston, March, 2017

This paper presents Splinter, a system that protects users’ queries on public datasets while achieving practical performance for many current web applications.

Recommended citation: Frank Wang, Catherine Yun, Shafi Goldwasser, and Vinod Vaikuntanathan. (2017). "Splinter: Practical Private Queries on Public Data." In Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI '17), Boston, March. https://www.usenix.org/system/files/conference/nsdi17/nsdi17-wang-frank.pdf