Privacy Inference Attack and Defense in Centralized and Federated Learning: A Comprehensive Survey

Article

Rao, Bosen, Zhang, Jiale, Wu, Di, Zhu, Chengcheng, Sun, Xiaobing and Chen, Bing. 2024. "Privacy Inference Attack and Defense in Centralized and Federated Learning: A Comprehensive Survey." IEEE Transactions on Artificial Intelligence. https://doi.org/10.1109/TAI.2024.3363670
Article Title

Privacy Inference Attack and Defense in Centralized and Federated Learning: A Comprehensive Survey

ERA Journal ID212760
Article CategoryArticle
AuthorsRao, Bosen, Zhang, Jiale, Wu, Di, Zhu, Chengcheng, Sun, Xiaobing and Chen, Bing
Journal TitleIEEE Transactions on Artificial Intelligence
Number of Pages22
Year2024
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN2691-4581
Digital Object Identifier (DOI)https://doi.org/10.1109/TAI.2024.3363670
Web Address (URL)https://ieeexplore.ieee.org/abstract/document/10429780
Abstract

The emergence of new machine learning methods has led to their widespread application across various domains, significantly advancing the field of artificial intelligence. However, the process of training and inferring machine learning models relies on vast amounts of data, which often includes sensitive private information. Consequently, the privacy and security of machine learning have encountered significant challenges. Several studies have demonstrated the vulnerability of machine learning to privacy inference attacks, but they often focus on specific scenarios, leaving a gap in understanding the broader picture. We provide a comprehensive review of privacy attacks in machine learning, focusing on two scenarios: centralized learning and federated learning. This paper begins by presenting the architectures of both centralized learning and federated learning, along with their respective application scenarios. It then conducts a comprehensive review and categorization of related infer...

KeywordsPrivacy inference attack; Machine learning security; Centralized and Federated learning; Privacy defense
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
4604. Cybersecurity and privacy
Public Notes

The accessible file is the accepted version of the paper. Please refer to the URL for the published version.

Byline AffiliationsYangzhou University, China
School of Mathematics, Physics and Computing
Nanjing University of Aeronautics and Astronautics, China
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