跨系统协同过滤推荐算法的隐私保护技术研究  

Privacy-preserving technology research on collaborative filtering recommendation algorithm between systems

作　　者：刘国丽[1,2] 李昂[1] 李艳萍[2] 于丽梅[2] Liu Guoli Li Ang Li Yanping Yu Limei(School of Computer Science ＆ Engineering, Hebei University of Technology, Tianjin 300401, China Langfang Branch, Hebei University of Technology, Langfang Hebei 065000, China)

机构地区：[1]河北工业大学计算机科学与软件学院,天津300401 [2]河北工业大学廊坊分校,河北廊坊065000

出　　处：《计算机应用研究》2017年第9期2804-2807,共4页Application Research of Computers

基　　金：河北省高等学校科学技术研究项目(ZD20131070)

摘　　要：针对跨系统协同过滤推荐中用户信息安全问题,提出一个安全计算模型。模型基于安全多方计算理论,使用轻量级分组密码算法LBlock加密第三方提供的数据,并用RSA密码系统管理密钥。以该模型为安全基础,结合随机扰乱技术,提出一种跨系统协同过滤推荐算法,其相似度计算方法可以有效防止不良商家伪造商品评分信息;安全矢量积的引入使得第三方与系统无法进行非法串通。实验证明,算法在防止用户信息泄露给协同推荐系统的同时,计算用户相似度更加精确,预测误差也显著降低。To solve the privacy security problem of the recommendation algorithm between systems, this paper developed a secure computation model based on the theory of secure multi-party computation. The model used LBlock, a lightweight block ci- pher algorithm, to encrypt the provided data by the third part, and used RSA public key cryptosystem to manage keys of LBlock. Applying this model to the collaborative fihering between systems with randomized perturbation techniques, the paper developed a new algorithm whose calculation method of similarity could protect the system from the attack of artificial users. It used secure vector to prevent the untrusted third party from colluding. Experiments show that algorithm not only has stronger ability to protect the user＇ s privacy disclosing to the system which is cooperated, but also has better quality of recommendation.

关 键 词：协同过滤推荐 隐私保持 安全多方计算 随机扰动 相似度 

分 类 号：TP309.2[自动化与计算机技术—计算机系统结构]