Vulnerabilities of NSPFL: Privacy-Preserving Federated Learning With Data Integrity Auditing
Jiahui Wu, Fucai Luo, Tiecheng Sun, Weizhe Zhang · IEEE Transactions on Information Forensics and Security · 2025
The secure and privacy-preserving federated learning scheme, NSPFL, aims to safeguard data privacy while also auditing data integrity. The solution provided by this scheme is highly novel. However, NSPFL has significant design shortcomings in terms of both privacy protection and data integrity verification. This work identifies specific issues within NSPFL and proposes effective countermeasures. Furthermore, our proposed solution can serve as a general approach for privacy-preserving multiparty computations, safeguarding privacy while enhancing efficiency.