基于云联邦的差分隐私保护动态推荐模型  

Differential Privacy-Preserving Dynamic Recommendation Model Based on Cloud Federation

作　　者：刘刚[1,2] 杨雯莉[1,2] 王同礼 李阳 LIU Gang;YANG Wenli;WANG Tongli;LI Yang(College of Computer Science and Technology,Harbin Engineering University,Harbin 150001,China;Modeling and Emulation in E-Government National Engineering Laboratory,Harbin Engineering University,Harbin 150001,China)

机构地区：[1]哈尔滨工程大学计算机科学与技术学院,哈尔滨150001 [2]哈尔滨工程大学电子政务建模仿真国家工程实验室,哈尔滨150001

出　　处：《信息网络安全》2023年第7期31-43,共13页Netinfo Security

基　　金：黑龙江省高等教育教学改革研究项目[SJGZ20200044];黑龙江省自然科学基金[LH2021F015];高端外国专家引进计划[G2021180008L]。

摘　　要：为提高推荐系统的准确性和个性化水平,同时保护用户的隐私,文章提出一种基于云联邦的差分隐私保护动态推荐模型(P2RCF)。该模型采用注意力机制动态调整融合长短期用户兴趣,增强推荐系统的灵活性,同时引入差分隐私技术和云联邦技术保护用户的隐私信息。文章在公共数据集上进行了实验,实验结果表明,该模型可以在保护用户数据隐私的同时提高推荐的准确性和个性化水平。This paper proposed a cloud-based federated differential privacy-Preserving dynamic recommendation model(P2RCF).The model employed an attention mechanism to dynamically adjust the fusion of short-term and long-term user interests,increasing the flexibility of the recommendation system.The paper also introduced differential privacy and cloud federation technologies to protect user privacy information.Experimental evaluations were conducted on public datasets to assess the performance of the proposed model.The results demonstrate that the model improves recommendation accuracy and personalization while preserving user data privacy.

关 键 词：推荐系统 云联邦 注意力机制 差分隐私 长短期兴趣 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]