"2018年《深度学习综合隐私安全性分析》：白盒推理攻击下的独立与联合学习"

Deep learning technology has become increasingly popular in recent years, but its susceptibility to privacy breaches has become a growing concern. In the study "Comprehensive Privacy Analysis of Deep Learning," authors Milad Nasr, Reza Shokri, and Amir Houmansadr aim to address this issue by conducting a thorough assessment of the privacy implications of deep neural networks. The researchers specifically focus on two types of learning methods: stand-alone and federated learning. The former involves training a model on a single centralized dataset, while the latter distributes the learning process across multiple devices or servers. Both methods are subjected to passive and active white-box inference attacks, where an adversary attempts to infer sensitive information from the model through various means. The study reveals that deep neural networks are vulnerable to these types of attacks due to their ability to remember information from their training data. This means that even in the absence of direct access to the training data, an adversary can still extract private information by analyzing the model's responses. To mitigate these risks, the authors propose several defense mechanisms that can enhance the privacy of deep learning systems. These include differential privacy, which adds noise to the training process to prevent the extraction of specific information, and homomorphic encryption, which allows for computations on encrypted data without decrypting it. In conclusion, the comprehensive analysis conducted in this study sheds light on the privacy challenges associated with deep learning technology. By identifying vulnerabilities and proposing potential solutions, the research aims to contribute to the development of more secure and privacy-preserving deep learning systems. As the field continues to advance, it is crucial to prioritize the protection of sensitive information to ensure the responsible and ethical use of deep learning technology.