计算机工程与应用2026，Vol.62Issue(12)：325-338,14.DOI:10.3778/j.issn.1002-8331.2504-0145
本地差分隐私下基于聚类的两阶段多任务学习算法
Two-Stage Clustering-Based Multi-Task Learning Algorithm Under Local Differential Privacy
摘要
Abstract
The widespread application of multi-task learning in data analysis amplifies privacy leakage risks.Traditional approaches relying on trusted servers,neglecting noise interference,and considering only task correlation result in insuffi-cient privacy protection and degraded model performance.This study proposes KTMTL,a two-stage clustering-based multi-task learning algorithm under local differential privacy(LDP),to jointly optimize privacy preservation and model utility.The first stage replaces the Euclidean distance with Huber distance in an improved K-means clustering algorithm,effectively suppressing outlier impacts induced by Laplace noise.The second stage constructs an interactive multi-task learning framework that simultaneously models feature-task correlations and optimizes parameters through gradient aggre-gation.Theoretical analysis confirms the algorithm's rigorous LDP compliance and bounded complexity.Experiments on School,ADNI,and synthetic datasets demonstrate KTMTL outperforms existing privacy-preserving algorithms(e.g.,DP-MTRL,DP-DMTL)by 10%-15%in prediction accuracy(AUC)and reduces normalized mean squared error(nMSE)by 8%-12%under equivalent LDP guarantees,while significantly outperforming comparable methods in computational efficiency.The proposed framework provides an efficient and robust solution for privacy-sensitive multi-task learning scenarios through synergistic optimization of privacy protection and task grouping quality.关键词
隐私保护/本地差分隐私/K-means聚类/多任务特征学习/多任务关系学习Key words
privacy protection/local differential privacy/K-means clustering/multi-task feature learning/multi-task rela-tionship learning分类
信息技术与安全科学引用本文复制引用
方贤进,程俊,张朋飞,方翔,陈家庆,王杰..本地差分隐私下基于聚类的两阶段多任务学习算法[J].计算机工程与应用,2026,62(12):325-338,14.基金项目
国家自然科学基金(61572034) （61572034）
云南省服务计算重点实验室开放课题(YNSC24116). （YNSC24116）
