一种改进的系统间隐私保持协同过滤推荐算法

针对系统间协同过滤推荐过程中的隐私泄露问题，以RSA公钥密码系统和安全多方计算SMC理论为基础，提出一个安全计算模型SCM，将安全计算模型SCM应用到系统间协同过滤中，得到一个有效的隐私保持协同过滤推荐算法。算法利用安全矢量积计算用户的相似度，防止了第三方的恶意串通。实验表明，该算法不但可以保护用户的隐私不泄露给协同合作的系统，而且提高了推荐算法的精度，特别是对用户数据稀疏的小站点。

Improved privacy-preserving collaborative filtering recommendation algorithm between sys-tems

To solve the privacy disclosure problem of the recommendation algorithm between systems, this paper addresses a secure computation model based on RSA public key cryptosystem and secure multi-party computation. Applying this model to the collaborative filtering between systems, an efficient privacy-preserving collaborative filtering recommender algorithm is proposed. The algorithm uses secure vector product to calculate the similarity of users, prevents the untrusted third party from colluding. Experimental results 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, especially for the small system of sparse data.