35 citations · 56 across the 42 of their papers we have counts for
6 papers · 1 filter
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…
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…
VerifyML: Obliviously Checking Model Fairness Resilient to Malicious Model Holder
Guowen Xu, Xingshuo Han, Gelei Deng +5
In this paper, we present VerifyML, the first secure inference framework to check the fairness degree of a given Machine learning (ML) model. VerifyML is generic and is immune to a…
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…
Privacy-preserving Decentralized Deep Learning with Multiparty Homomorphic Encryption
Guowen Xu, Guanlin Li, Shangwei Guo +2
Decentralized deep learning plays a key role in collaborative model training due to its attractive properties, including tolerating high network latency and less prone to single-po…
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…