An algorithm for efficient privacy-preserving item-based collaborative filtering
An algorithm for efficient privacy-preserving item-based collaborative filtering
复制标题
一种基于项目的高效隐私保护协同过滤算法
DOI：
10.1016/j.future.2014.11.003
复制
发表时间：
2016-02-01
影响因子：
7.5
通讯作者：
Gu, Ning
中科院分区：
文献类型：
--
作者：
Li, Dongsheng;Chen, Chao;Gu, Ning
协同过滤（CF）方法被现有的推荐系统广泛采用，它能够基于用户的历史行为分析和预测用户对新生成项目的“评分”或“偏好”。然而，在这个过程中出现了隐私问题，因为推荐服务器收集了敏感的用户隐私数据。最近提出的隐私保护协同过滤（PPCF）方法，使用计算密集型的密码技术或数据扰动技术，在实际的在线服务中并不适用。在本文中，提出了一种高效的基于项目的隐私保护协同过滤算法，它能够在在线推荐过程中保护用户隐私，同时不影响推荐的准确性和效率。使用奈飞奖数据集对所提出的方法进行了评估。实验结果表明，所提出的方法在推荐效率方面分别比基于随机扰动的PPCF解决方案和基于同态加密的PPCF解决方案高出14倍和386倍以上，同时实现了相似甚至更好的推荐准确性。© 2014爱思唯尔有限公司。保留所有权利。
Collaborative filtering (CF) methods are widely adopted by existing recommender systems, which can analyze and predict user "ratings" or "preferences" of newly generated items based on user historical behaviors. However, privacy issue arises in this process as sensitive user private data are collected by the recommender server. Recently proposed privacy-preserving collaborative filtering (PPCF) methods, using computation-intensive cryptography techniques or data perturbation techniques are not appropriate in real online services. In this paper, an efficient privacy-preserving item-based collaborative filtering algorithm is proposed, which can protect user privacy during online recommendation process without compromising recommendation accuracy and efficiency. The proposed method is evaluated using the Netflix Prize dataset. Experimental results demonstrate that the proposed method outperforms a randomized perturbation based PPCF solution and a homomorphic encryption based PPCF solution by over 14X and 386X, respectively, in recommendation efficiency while achieving similar or even better recommendation accuracy. (C) 2014 Elsevier B.V. All rights reserved.