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.

The advent of the big data era has brought unprecedented data demands. The integration of computing resources with network resources in the computing force network enables the possibility of distributed collaborative training. However, unencrypted collaborative training is vulnerable to threats such as gradient inversion attacks and model theft. To address this issue, the data in collaborative training are usually protected by cryptographic methods. However, the semantic meaninglessness of encrypted data makes it difficult to prevent potential data poisoning attacks and free-riding attacks. In this paper, we propose a fairness guarantee approach for privacy-preserving collaborative training, employing blockchain technology to enable participants to share data and exclude potential violators from normal users. We utilize a cryptography-based secure aggregation method to prevent data leakage during blockchain transactions, and employ a contribution evaluation method for encrypted data to prevent data poisoning and free-riding attacks. Additionally, utilizing Shamir’s secret sharing for secret key negotiation within the group, the negotiated key is directly introduced as noise into the model, ensuring the encryption process is computationally lightweight. Decryption is efficiently achieved through the aggregation of encrypted models within the group, without incurring additional computational costs, thereby enhancing the computational efficiency of the encryption and decryption processes. Finally, the experimental results demonstrate the effectiveness and efficiency of our proposed approach.

As quantum machine learning modules become increasingly integrated into NISQ-era infrastructures, it remains unclear whether intrinsic device noise can be regarded as a passive defense against adversarial examples, or whether it in fact introduces a new attack surface. To answer this question, we propose a noise-aware four-path evaluation protocol that decouples the noise assumed at attack generation from the noise present at inference, and we systematically test it on a 4-qubit variational quantum classifier over four datasets with depolarizing probabilities in the range $p\in [0,0.3]$, using both standard gradient attacks and expectation over transformation (EOT)-based attacks. The results show that for some datasets, higher noise does suppress attacks, whereas for others attacks remain effective even at $p=0.3$, and in several cases a moderate noise level even maximizes the attack success rate. Moreover, we find that adversarial examples generated under moderate noise often attack the clean model more successfully than those generated in an ideal setting, demonstrating that noise can be actively exploited by an adversary to discover more transferable adversarial directions. Therefore, ambient noise should not be treated as a built-in security guarantee, and future quantum machine learning (QML) robustness evaluations must explicitly model such noise-aware threats.