Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy

Author:

Ruan Wenqiang1,Xu Mingxin1,Fang Wenjing2,Wang Li2,Wang Lei2,Han Weili1

Affiliation:

1. Fudan University,Laboratory of Data Analytics and Security

2. Ant Group

Publisher

IEEE

Reference82 articles.

1. Decision transformer: Reinforcement learning via sequence modeling;chen;Advances in neural information processing systems,2021

2. Combining Differential Privacy and Secure Multiparty Computation

3. Differentially private empirical risk minimization;chaudhuri;Journal of Machine Learning Research,2011

4. Ca{pc} learning: Confidential and private collaborative learning;choquette-choo;International Conference on Learning Representations,2021

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4. Differential Privacy with Data Removal for Online Happiness Assessment;Lecture Notes in Computer Science;2024

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