Adaptive Feature Representation Learning for Privacy-Fairness Joint Optimization
Chao Ma, Mingkai Dai, Zhibo Guan, Zi Ye, Yikai Hou, Xiaoyu Wang, Hai Huang · Applied Sciences · 2025
Coded text representations often contain a large amount of personal sensitive information, which can easily lead to problems such as privacy leakage and model prediction bias. Most of the existing methods focus on optimizing a single objective, making it difficult to achieve an effective balance between model performance, fairness and privacy protection. For this reason, this paper proposes a new adaptive feature representation learning method, AMF-DP (adaptive matrix factorization with differential privacy). The method combines adaptive matrix factorization with a differential privacy technique to effectively improve the fairness of the model while realizing privacy protection. The experimental results show that AMF-DP is able to achieve a better balance between privacy protection, fairness, and model performance, providing a new way of thinking for text feature representation learning that takes into account multi-objective optimization.