Locally differentially private item-based collaborative filtering
Locally differentially private item-based collaborative filtering
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DOI：
10.1016/j.ins.2019.06.021
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Taolin Guo;Junzhou Luo;K. Dong;Ming Yang
中科院分区：
文献类型：
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作者：
Taolin Guo;Junzhou Luo;K. Dong;Ming Yang
最近，基于项目的协同过滤吸引了大量的关注。它基于用户报告的历史数据（即，他们已经感兴趣的项目）。在推荐服务不完全可信的情况下，所报告的历史数据导致显著的隐私风险。许多研究都集中在发展不同的隐私机制，以保护个人数据的各种建议。然而，这些机制大多不能确保建议的准确性。这个问题的主要原因是，这些方法直接从扰动数据计算相似性。因此，计算的相似度总是不准确的，并且这种不准确的相似度最终导致不准确的推荐结果。本文提出了一种基于局部差异隐私项的协同过滤框架，在用户端保护用户的隐私历史数据，在服务器端重构相似度以保证推荐的准确性。通过估计没有对每一对项目进行评级的用户的数量，对每一对项目的相似性进行重建。最终的推荐是由重建的相似性产生的。实验结果表明，我们提出的方法显着优于国家的最先进的方法在推荐的准确性和隐私和准确性之间的权衡。
Recently, item-based collaborative filtering has attracted a lot of attention. It recommends to users new items which may be of interests to them, based on their reported historical data (i.e., the items they have already been interested in). The reported historical data leads to significant privacy risks in case that the recommending service is not fully trusted. Many researches have focused on developing differential privacy mechanisms to protect personal data in various recommendations. However, most of these mechanisms can not ensure accuracy of the recommendations. The main reason for this problem is that these methods compute similarity directly from the perturbation data. The computed similarity is thus always inaccurate and this inaccurate similarity finally leads to inaccurate recommendation results. In this paper, we propose a locally differentially private item-based collaborative filtering framework, which protects users’ private historical data on the user-side, and on the server-side reconstructs the similarity to ensure recommendation accuracy. The similarities are reconstructed for every pair of items, by estimating the number of users who have rated neither, either one, or both of them. The final recommendation is generated by the reconstructed similarities. Experimental results show that our proposed method significantly outperforms the state-of-the-art methods in terms of the recommendation accuracy and the trade-off between privacy and accuracy.