A Data Attack Detection Framework for Cryptography-Based Secure Aggregation Methods in 6G Intelligent Applications

Abstract

Eagerly anticipated, 6G networks are attributed with a variety of characteristics by researchers. A pivotal characteristic of 6G networks is the deep integration of sensing and networking, along with intelligent network applications operating on top of this infrastructure. To optimally harness the data collected by sensors distributed across various locations, the training paradigm of the new generation of 6G intelligence applications aligns naturally with the federated-learning paradigm. The exposure of gradients in federated learning to inversion attacks is a critical concern. To address this, cryptography-based secure aggregation methods are commonly implemented to protect the privacy and confidentiality of gradients. However, the semantic meaninglessness of encrypted data makes it difficult to assess the correctness, availability, and source legitimacy of participants’ data. In this paper, we propose a data attack detection framework for cryptography-based secure aggregation methods in 6G intelligence applications that address the security vulnerabilities associated with encrypted data obscurity. We employ a suite of encrypted-data-auditing techniques to prevent data-aggregation errors, data poisoning, and illegal data sources. Additionally, we have compared a series of promising security methods, analyzed, and provided recommendations for the most suitable security approaches in specific 6G scenarios.