VerifyML: Obliviously Checking Model Fairness Resilient to Malicious Model Holder
Authors: Guowen Xu, Xingshuo Han, Gelei Deng, Tianwei Zhang, Shengmin Xu, Jianting Ning, Anjia Yang, Hongwei Li | Published: 2022-10-16
Multi-Party Computation
Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation
Authors: Qiongxiu Li, Jaron Skovsted Gundersen, Katrine Tjell, Rafal Wisniewski, Mads Græsbøll Christensen | Published: 2022-09-16
Classification Protocols with Minimal Disclosure
Authors: Jinshuo Dong, Jason Hartline, Aravindan Vijayaraghavan | Published: 2022-09-06
BlindFL: Vertical Federated Machine Learning without Peeking into Your Data
Authors: Fangcheng Fu, Huanran Xue, Yong Cheng, Yangyu Tao, Bin Cui | Published: 2022-06-16
Towards Privacy-Preserving and Verifiable Federated Matrix Factorization
Authors: Xicheng Wan, Yifeng Zheng, Qun Li, Anmin Fu, Mang Su, Yansong Gao | Published: 2022-04-04 | Updated: 2022-06-11
Efficient Dropout-resilient Aggregation for Privacy-preserving Machine Learning
Authors: Ziyao Liu, Jiale Guo, Kwok-Yan Lam, Jun Zhao | Published: 2022-03-31
Scalable Privacy-Preserving Distributed Learning
Authors: David Froelicher, Juan R. Troncoso-Pastoriza, Apostolos Pyrgelis, Sinem Sav, Joao Sa Sousa, Jean-Philippe Bossuat, Jean-Pierre Hubaux | Published: 2020-05-19 | Updated: 2021-07-14