302 citations · 547 across the 18 of their papers we have counts for
18 papers
DeFeed: Secure Decentralized Cross-Contract Data Feed in Web 3.0 for Connected Autonomous Vehicles
Xingchen Sun, Runhua Xu, Wei Ni +2
Smart contracts have been a topic of interest in blockchain research and are a key enabling technology for Connected Autonomous Vehicles (CAVs) in the era of Web 3.0. These contrac…
Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning
Runhua Xu, Shiqi Gao, Chao Li +2
Federated learning (FL) is inherently susceptible to privacy breaches and poisoning attacks. To tackle these challenges, researchers have separately devised secure aggregation mech…
TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning
Runhua Xu, Bo Li, Chao Li +3
Federated learning is a computing paradigm that enhances privacy by enabling multiple parties to collaboratively train a machine learning model without revealing personal data. How…
Blockchain Takeovers in Web 3.0: An Empirical Study on the TRON-Steem Incident
Chao Li, Runhua Xu, Balaji Palanisamy +4
A fundamental goal of Web 3.0 is to establish a decentralized network and application ecosystem, thereby enabling users to retain control over their data while promoting value exch…
How Hard is Takeover in DPoS Blockchains? Understanding the Security of Coin-based Voting Governance
Chao Li, Balaji Palanisamy, Runhua Xu +3
Delegated-Proof-of-Stake (DPoS) blockchains, such as EOSIO, Steem and TRON, are governed by a committee of block producers elected via a coin-based voting system. We recently witne…
Liquid Democracy in DPoS Blockchains
Chao Li, Runhua Xu, Li Duan
Voting mechanisms play a crucial role in decentralized governance of blockchain systems. Liquid democracy, also known as delegative voting, allows voters to vote directly or delega…
Cross-Consensus Measurement of Individual-level Decentralization in Blockchains
Chao Li, Balaji Palanisamy, Runhua Xu +1
Decentralization is widely recognized as a crucial characteristic of blockchains that enables them to resist malicious attacks such as the 51% attack and the takeover attack. Prior…
DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting
Runhua Xu, Nathalie Baracaldo, Yi Zhou +3
Federated learning has emerged as a privacy-preserving machine learning approach where multiple parties can train a single model without sharing their raw training data. Federated…
Privacy-Preserving Machine Learning: Methods, Challenges and Directions
Runhua Xu, Nathalie Baracaldo, James Joshi
Machine learning (ML) is increasingly being adopted in a wide variety of application domains. Usually, a well-performing ML model relies on a large volume of training data and high…
FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data
Runhua Xu, Nathalie Baracaldo, Yi Zhou +3
Federated learning (FL) has been proposed to allow collaborative training of machine learning (ML) models among multiple parties where each party can keep its data private. In this…
SteemOps: Extracting and Analyzing Key Operations in Steemit Blockchain-based Social Media Platform
Chao Li, Balaji Palanisamy, Runhua Xu +2
Advancements in distributed ledger technologies are driving the rise of blockchain-based social media platforms such as Steemit, where users interact with each other in similar way…
Blockchain-based Transparency Framework for Privacy Preserving Third-party Services
Runhua Xu, Chao Li, James Joshi
Increasingly, information systems rely on computational, storage, and network resources deployed in third-party facilities such as cloud centers and edge nodes. Such an approach fu…
Revisiting Secure Computation Using Functional Encryption: Opportunities and Research Directions
Runhua Xu, James Joshi
Increasing incidents of security compromises and privacy leakage have raised serious privacy concerns related to cyberspace. Such privacy concerns have been instrumental in the cre…
NN-EMD: Efficiently Training Neural Networks using Encrypted Multi-Sourced Datasets
Runhua Xu, James Joshi, Chao Li
Training a machine learning model over an encrypted dataset is an existing promising approach to address the privacy-preserving machine learning task, however, it is extremely chal…
NF-Crowd: Nearly-free Blockchain-based Crowdsourcing
Chao Li, Balaji Palanisamy, Runhua Xu +2
Advancements in distributed ledger technologies are rapidly driving the rise of decentralized crowdsourcing systems on top of open smart contract platforms like Ethereum. While dec…
HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning
Runhua Xu, Nathalie Baracaldo, Yi Zhou +2
Federated learning has emerged as a promising approach for collaborative and privacy-preserving learning. Participants in a federated learning process cooperatively train a model b…
CryptoNN: Training Neural Networks over Encrypted Data
Runhua Xu, James B. D. Joshi, Chao Li
Emerging neural networks based machine learning techniques such as deep learning and its variants have shown tremendous potential in many application domains. However, they raise s…
Scalable and Privacy-preserving Design of On/Off-chain Smart Contracts
Chao Li, Balaji Palanisamy, Runhua Xu
The rise of smart contract systems such as Ethereum has resulted in a proliferation of blockchain-based decentralized applications including applications that store and manage a wi…