A Survey on Multi-User Privacy Issues in Edge Intelligence: State of the Art, Challenges, and Future Directions
Xiuwen Liu, Bowen Li, Sirui Chen, Zhiqiang Xu · Electronics · 2025
Edge intelligence is an emerging paradigm generated by the deep integration of artificial intelligence (AI) and edge computing. It enables data to remain at the edge without being sent to remote cloud servers, lowering response time, saving bandwidth resources, and opening up new development opportunities for multi-user intelligent services (MISs). Although edge intelligence can address the problems of centralized MISs, its inherent characteristics also introduce new challenges, potentially leading to serious security issues. Malicious attackers may use inference attacks and other methods to access private information and upload toxic updates that disrupt the model and cause severe damage. This paper provides a comprehensive review of multi-user privacy protection mechanisms and compares the network architectures under centralized and edge intelligence paradigms, exploring the privacy and security issues introduced by edge intelligence. We then investigate the state-of-the-art defense mechanisms under the edge intelligence paradigm and provide a systematic classification. Through experiments, we compare the privacy protection and utility trade-offs of existing methods. Finally, we propose future research directions for privacy protection in MISs under the edge intelligence paradigm, aiming to promote the development of user privacy protection frameworks.