A Privacy-Preserving Framework for Trust-Oriented Point-of-Interest Recommendation
A Privacy-Preserving Framework for Trust-Oriented Point-of-Interest Recommendation
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DOI：
10.1109/access.2017.2765317
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发表时间：
2018
期刊：
影响因子：
3.9
通讯作者：
An Liu;Weiqi Wang;Zhixu Li;Guanfeng Liu;Qing Li;Xiaofang Zhou;Xiangliang Zhang
中科院分区：
文献类型：
--
作者：
An Liu;Weiqi Wang;Zhixu Li;Guanfeng Liu;Qing Li;Xiaofang Zhou;Xiangliang Zhang
兴趣点（POI）推荐最近引起了许多人的兴趣，因为它在帮助用户探索新地点以及帮助基于位置的服务（LBS）提供商进行精准营销方面具有巨大潜力。与传统推荐系统中的用户-项目评分矩阵相比，POI推荐中的用户-位置签到矩阵通常更加稀疏，这使得臭名昭著的冷启动问题在POI推荐中更加突出。面向信任的推荐是解决这个问题的有效方法，但它要求推荐者能够访问用户签到和信任数据。然而，在实践中，这些数据通常由不同的企业拥有，他们不愿意与推荐者共享其数据，主要是出于隐私和法律方面的考虑。在本文中，我们提出了一个隐私保护框架，以提高数据所有者与不可信企业共享数据的意愿。更具体地说，我们利用部分同态加密来设计两种用于保护隐私、面向信任的 POI 推荐的协议。通过离线加密和并行计算，这些协议可以有效保护推荐涉及各方的隐私数据。我们证明所提出的协议对于半诚实的对手是安全的。对合成数据和真实数据的实验表明，我们的协议可以以可接受的计算和通信成本实现隐私保护。
Point-of-interest (POI) recommendation has attracted many interests recently because of its significant potential for helping users to explore new places and helping location-based service (LBS) providers to carry out precision marketing. Compared with the user-item rating matrix in conventional recommender systems, the user-location check-in matrix in POI recommendation is usually much more sparse, which makes the notorious cold start problem more prominent in POI recommendation. Trust-oriented recommendation is an effective way to deal with this problem but it requires that the recommender has access to user check-in and trust data. In practice, however, these data are usually owned by different businesses who are not willing to share their data with the recommender mainly due to privacy and legal concerns. In this paper, we propose a privacy-preserving framework to boost data owners willingness to share their data with untrustworthy businesses. More specifically, we utilize partially homomorphic encryption to design two protocols for privacy-preserving trust-oriented POI recommendation. By offline encryption and parallel computing, these protocols can efficiently protect the private data of every party involved in the recommendation. We prove that the proposed protocols are secure against semi-honest adversaries. Experiments on both synthetic data and real data show that our protocols can achieve privacy-preserving with acceptable computation and communication cost.