电子科技大学学报2025，Vol.54Issue(3)：432-441,10.DOI:10.12178/1001-0548.2024081
结合混洗器的差分隐私矩阵分解推荐算法
Differential privacy matrix factorization recommendation algorithm combined with shuffler
摘要
Abstract
Recommendation systems require extensive user data for computations,posing a risk to user privacy.While differential privacy techniques have been used to protect user privacy,in untrusted server scenarios,existing methods suffer from reduced recommendation effectiveness due to excessive noise injection.To address this issue,we propose a differential privacy matrix factorization recommendation algorithm that incorporates a shuffler to leverage the privacy amplification effect of shuffling operations for noise reduction.Additionally,we address the problem of recommendation performance degradation caused by data sparsity by adding noise to the top k gradients locally,thus achieving a better balance between privacy protection and data utility optimization.Theoretical and experimental results confirm that this algorithm not only effectively enhances privacy protection but also yields excellent recommendation results,demonstrating its promising application potential.关键词
矩阵分解/差分隐私/混洗器/推荐系统Key words
matrix factorization/differential privacy/shuffler/recommendation system分类
信息技术与安全科学引用本文复制引用
叶建梅,杨久裕,陈钱宏,邓江洲,王永..结合混洗器的差分隐私矩阵分解推荐算法[J].电子科技大学学报,2025,54(3):432-441,10.基金项目
国家自然科学基金(62272077,72301050) （62272077,72301050）
中国博士后科学基金(2021M702321) （2021M702321）
