哈尔滨商业大学学报（自然科学版）2026，Vol.42Issue(2)：131-140,10.
基于方波机制的差分隐私域自适应学习方法
Domain adaptive learning method under differential privacy via square wave mechanism
摘要
Abstract
Domain adaptation became an increasingly important technique for addressing distributional shifts and label scarcity across domains.However,existing privacy-preserving methods,such as differential privacy,often suffered from excessive noise injection and limited model performance,especially in untrusted and high-dimensional settings.To address these challenges,this paper proposed a local differentially private square wave-based domain adaptation method called LDP-SWDA.Specifically,LDP-SWDA applied square wave-based local perturbation to the feature covariance structure with high probability.A gradient-based correction method was employed to restore the positive semi-definiteness of the covariance matrix,which might have been compromised by perturbation.Theoretical analysis was conducted on the privacy of LDP-SWDA,and the effectiveness of LDP-SWDA was evaluated on two standard domain adaptive learning datasets.The results indicated that LDP-SWDA exhibited good practicality and robustness in high-dimensional and privacy-sensitive scenarios,which had research significance.关键词
域自适应学习/本地差分隐私/方波机制/隐私保护/高维Key words
domain adaptive learning/local differential privacy/square wave mechanism/privacy protection/high-dimensional分类
信息技术与安全科学引用本文复制引用
方翔,方贤进,程俊,陈家庆,王杰..基于方波机制的差分隐私域自适应学习方法[J].哈尔滨商业大学学报（自然科学版）,2026,42(2):131-140,10.基金项目
国家自然科学基金(61572034) （61572034）
