中圖分類號：TP393
文獻標識碼：A
DOI:10.19358/j.issn.2097-1788.2023.05.007
引用格式：黎蘭蘭,張信明.面向分類任務的隱私保護協(xié)作學習技術［J］.網絡安全與數據治理,，2023,，42（5）：36-43.

Privacy-preserving collaborative learning technology for classification

Abstract： With the release of relevant laws and regulations and the awakening of people’s privacy awareness, the requirements for privacy protection are constantly increasing. Aiming at the scenario of classification, this paper proposes a Privacypreserving Collaborative Learning Technology for Classification (PCTC) that emphasizes both privacy and utility. In terms of privacy, homomorphic encryption and differential privacy are combined and a secure aggregation mechanism is designed to achieve more robust privacy protection. In terms of utility, the calculation factor of information entropy is introduced to measure the credibility of labels, which can reduce the impact of labeling noise on model performance. Finally, the security analysis of the proposed scheme is carried out, and the experiments are implemented on public datasets. The results show that PCTC significantly improves model performance while ensuring privacy and security of the data, and has broad application prospects.

Key words : privacy preservation; data labeling; classification task; homomorphic encryption; differential privacy