机器学习模型安全与隐私研究综述

In the era of big data, the breakthroughs in theories and technologies such as deep learning, reinforcement learning, and distributed learning have provided strong support for machine learning at the data and algorithm levels and simultaneously promoted the large-scale and industrialized development of machine learning. However, although machine learning models have excellent performance in real-world applications, they still face many security threats. The security and privacy threats faced by machine learning at the data layer, model layer, and application layer exhibit the characteristics of diversity, concealment, and dynamic evolution. The security and privacy issues of machine learning have attracted extensive attention from academia and industry. A large number of scholars have conducted in-depth research on the security and privacy issues of models from the perspectives of attack and defense respectively and have proposed a series of attack and defense methods. This paper reviews the security and privacy issues of machine learning and systematically summarizes and scientifically generalizes the existing research work. At the same time, it clarifies the advantages and disadvantages of the current research. Finally, it discusses the challenges currently faced by the research on the security and privacy protection of machine learning models and the potential research directions in the future, aiming to provide guidance for subsequent scholars to further promote the development and application of the research on the security and privacy protection of machine learning models.

在大数据时代下,深度学习、强化学习以及分布式学习等理论和技术取得的突破性进展,为机器学习提供了数据和算法层面强有力的支撑,同时促进了机器学习的规模化和产业化发展.然而,尽管机器学习模型在现实应用中有着出色的表现,但其本身仍然面临着诸多的安全威胁.机器学习在数据层、模型层以及应用层面临的安全和隐私威胁呈现出多样性、隐蔽性和动态演化的特点.机器学习的安全和隐私问题吸引了学术界和工业界的广泛关注,一大批学者分别从攻击和防御的角度对模型的安全和隐私问题进行了深入的研究,并且提出了一系列的攻防方法.回顾了机器学习的安全和隐私问题,并对现有的研究工作进行了系统的总结和科学的归纳,同时明确了当前研究的优势和不足.最后探讨了机器学习模型安全与隐私保护研究当前所面临的挑战以及未来潜在的研究方向,旨在为后续学者进一步推动机器学习模型安全与隐私保护研究的发展和应用提供指导.