PrivColl: Practical Privacy-Preserving Collaborative Machine Learning
european symposium on research in computer security(2020)
摘要
Collaborative learning enables two or more participants, each with their own training dataset, to collaboratively learn a joint model. It is desirable that the collaboration should not cause the disclosure of either the raw datasets of each individual owner or the local model parameters trained on them. This privacy-preservation requirement has been approached through differential privacy mechanisms, homomorphic encryption (HE) and secure multiparty computation (MPC), but existing attempts may either introduce the loss of model accuracy or imply significant computational and/or communicational overhead.
更多查看译文
关键词
machine learning,privacy-preserving
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络