activity
20192025
most citedHybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning

302 citations · 547 across the 18 of their papers we have counts for

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Showing cs.CRShow all

14 papers · 1 filter

cs.CR2025

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…

cs.CR2025★ 2 cited

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…

cs.CR2025★ 60 cited

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…

cs.CR2023★ 1 cited

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…

cs.CR2023

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…

cs.CR2023★ 9 cited

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…

cs.CR2022

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…

cs.CR2021★ 22 cited

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…

cs.CR2021★ 27 cited

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…

cs.CR2020★ 1 cited

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…

cs.CR2020★ 1 cited

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…

cs.CR2019★ 302 cited

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…

cs.CR2019★ 9 cited

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…

cs.CR2019★ 10 cited

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…