most citedHercules: Boosting the Performance of Privacy-preserving Federated Learning

1 citations · 3 across the 5 of their papers we have counts for

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cs.CR2024★ 1 cited

FairRelay: Fair and Cost-Efficient Peer-to-Peer Content Delivery through Payment Channel Networks

Jingyu Liu, Yingjie Xue, Zifan Peng +2

Peer-to-Peer (P2P) content delivery, known for scalability and resilience, offers a decentralized alternative to traditional centralized Content Delivery Networks (CDNs). A signifi…

cs.CR2022

Efficiency Boosting of Secure Cross-platform Recommender Systems over Sparse Data

Hao Ren, Guowen Xu, Tianwei Zhang +4

Fueled by its successful commercialization, the recommender system (RS) has gained widespread attention. However, as the training data fed into the RS models are often highly sensi…

cs.CR2022

New Secure Sparse Inner Product with Applications to Machine Learning

Guowen Xu, Shengmin Xu, Jianting Ning +4

Sparse inner product (SIP) has the attractive property of overhead being dominated by the intersection of inputs between parties, independent of the actual input size. It has intri…

cs.CR2022★ 1 cited

Hercules: Boosting the Performance of Privacy-preserving Federated Learning

Guowen Xu, Xingshuo Han, Shengmin Xu +4

In this paper, we address the problem of privacy-preserving federated neural network training with users. We present Hercules, an efficient and high-precision training framewor…

cs.CR2022★ 1 cited

SIMC 2.0: Improved Secure ML Inference Against Malicious Clients

Guowen Xu, Xingshuo Han, Tianwei Zhang +5

In this paper, we study the problem of secure ML inference against a malicious client and a semi-trusted server such that the client only learns the inference output while the serv…