1. Nicolas Papernot Patrick McDaniel Arunesh Sinha and Michael Wellman. 2016. Towards the science of security and privacy in machine learning. arXiv preprint arXiv:1611.03814(2016). Nicolas Papernot Patrick McDaniel Arunesh Sinha and Michael Wellman. 2016. Towards the science of security and privacy in machine learning. arXiv preprint arXiv:1611.03814(2016).
2. Progress and Future Challenges of Security Attacks and Defense Mechanisms in Machine Learning;Li Xinjiao;Journal of Software,2021
3. Martin Strobel and Reza Shokri . 2022. Data Privacy and Trustworthy Machine Learning . IEEE Security & Privacy 01 ( 2022 ), 2–7. Martin Strobel and Reza Shokri. 2022. Data Privacy and Trustworthy Machine Learning. IEEE Security & Privacy01 (2022), 2–7.
4. Ji Liu Jizhou Huang Yang Zhou Xuhong Li Shilei Ji Haoyi Xiong and Dejing Dou. 2022. From distributed machine learning to federated learning: A survey. Knowledge and Information Systems(2022) 1–33. Ji Liu Jizhou Huang Yang Zhou Xuhong Li Shilei Ji Haoyi Xiong and Dejing Dou. 2022. From distributed machine learning to federated learning: A survey. Knowledge and Information Systems(2022) 1–33.
5. Christian Rechberger and Roman Walch . 2022. Privacy-preserving machine learning using cryptography . In Security and Artificial Intelligence . Springer , 109–129. Christian Rechberger and Roman Walch. 2022. Privacy-preserving machine learning using cryptography. In Security and Artificial Intelligence. Springer, 109–129.