More Efficient and Scalable Post-Quantum Multi-Signatures
Dr. Muhammed Esgin, Assoc. Prof. Ron Steinfeld, Assoc. Prof. John Tsz Hon Yuen, Prof. Joseph Liu
Monash University
This project develops two post-quantum multi-signature schemes, providing diverse security assumptions and tradeoffs. It contributes to enhancing Ethereum’s scalability and security through advanced cryptographic methods.
Balancing Anonymity and Compliance in Financial and Other Transactions
The research focuses on privacy-preserving blueprints, enhancing security and compliance in financial transactions. It aims to enable anonymous credentials while allowing traceability under specific legal circumstances.
This project bridges theoretical and practical aspects of the Fiat-Shamir transform, focusing on integrating the SAFE API into zk-proof frameworks to enhance their adoption.
Zero-knowledge proof has become more widely used in blockchain applications. This project plans to formally verify the security guarantees of ZK compilers to enhance their robustness in blockchain environments.
Digital auction mechanisms require security, privacy, and scalability. This project aims to develop auctions that satisfy these requirements, enabling secure and efficient asset price discovery in online marketplaces.
This project designs and implements SNARK-friendly post-quantum signatures to enable efficient and generic aggregation schemes, enhancing Ethereum’s ecosystem with advanced cryptographic tools.
This project focuses on designing cryptographic primitives for ZK applications, enabling privacy-preserving computation verification and enhancing the security of Ethereum rollup solutions.
Cryptanalysis of Symmetric Cryptographic Primitives
This project analyzes the efficiency and security of symmetric cryptographic primitives used in ZKPs, contributing to the reliability and scalability of Ethereum-based solutions.
This project develops tools to analyze, verify, and optimize polynomial constraint systems used in ZK protocols, aiming to enhance their usability and performance.
This project integrates economic modeling and ZKPs to enhance blockchain-based AI frameworks, ensuring privacy and security in machine learning applications.
