733 citations · 1.3k across the 7 of their papers we have counts for
7 papers
MISO: Legacy-compatible Privacy-preserving Single Sign-on using Trusted Execution Environments
Rongwu Xu, Sen Yang, Fan Zhang +1
Single sign-on (SSO) allows users to authenticate to third-party applications through a central identity provider. Despite their wide adoption, deployed SSO systems suffer from pri…
Understand Waiting Time in Transaction Fee Mechanism: An Interdisciplinary Perspective
Luyao Zhang, Fan Zhang
Blockchain enables peer-to-peer transactions in cyberspace without a trusted third party. The rapid growth of Ethereum and smart contract blockchains generally calls for well-desig…
SoK: MEV Countermeasures: Theory and Practice
Sen Yang, Fan Zhang, Ken Huang +3
Blockchains offer strong security guarantees, but they cannot protect the ordering of transactions. Powerful players, such as miners, sequencers, and sophisticated bots, can reap s…
zkBridge: Trustless Cross-chain Bridges Made Practical
Tiancheng Xie, Jiaheng Zhang, Zerui Cheng +5
Blockchains have seen growing traction with cryptocurrencies reaching a market cap of over 1 trillion dollars, major institution investors taking interests, and global impacts on g…
Empirical Analysis of EIP-1559: Transaction Fees, Waiting Time, and Consensus Security
Yulin Liu, Yuxuan Lu, Kartik Nayak +3
A transaction fee mechanism (TFM) is an essential component of a blockchain protocol. However, a systematic evaluation of the real-world impact of TFMs is still absent. Using rich…
Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contract Execution
Raymond Cheng, Fan Zhang, Jernej Kos +6
Smart contracts are applications that execute on blockchains. Today they manage billions of dollars in value and motivate visionary plans for pervasive blockchain deployment. While…
Stealing Machine Learning Models via Prediction APIs
Florian Tramèr, Fan Zhang, Ari Juels +2
Machine learning (ML) models may be deemed confidential due to their sensitive training data, commercial value, or use in security applications. Increasingly often, confidential ML…