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APPLET: a privacy-preserving framework for location-aware recommender system

一种面向位置感知推荐系统的隐私保框架

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Abstract

Location-aware recommender systems that use location-based ratings to produce recommendations have recently experienced a rapid development and draw significant attention from the research community. However, current work mainly focused on high-quality recommendations while underestimating privacy issues, which can lead to problems of privacy. Such problems are more prominent when service providers, who have limited computational and storage resources, leverage on cloud platforms to fit in with the tremendous number of service requirements and users. In this paper, we propose a novel framework, namely APPLET, for protecting user privacy information, including locations and recommendation results, within a cloud environment. Through this framework, all historical ratings are stored and calculated in ciphertext, allowing us to securely compute the similarities of venues through Paillier encryption, and predict the recommendation results based on Paillier, commutative, and comparable encryption. We also theoretically prove that user information is private and will not be leaked during a recommendation. Finally, empirical results over a real-world dataset demonstrate that our framework can efficiently recommend POIs with a high degree of accuracy in a privacy-preserving manner.

创新点

作为提供个性化位置服务的一种重要手段, 高速、高效的位置感知推荐服务成为当前研究的热点。然而, 涉及多方参与的传统推荐流程存在着用户私密信息复制、盗取等安全威胁, 给用户的隐私保护带来了新的挑战, 尤其是当服务提供者将数据外包给第三方云平台时, 隐私泄露问题会更加凸显。为解决上述问题, 本文提出了一种面向位置感知推荐系统的隐私保护框架, 通过利用Paillier加密、可交换加密和可比较加密实现位置服务的安全推荐。通过理论证明和分析, 在该框架下, 用户的位置隐私信息在推荐过程中得到了有效保护。最后, 本文设计实现该框架并通过真实数据集进行测试, 测试结果表明该框架能够准确高效的为用户返回推荐结果。

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Authors and Affiliations

  1. School of Computer Science and Technology, Xidian University, Xi’an, 710071, China

    Xindi Ma, Jianfeng Ma & Di Lu

  2. School of Cyber Engineering, Xidian University, Xi’an, 710071, China

    Xindi Ma, Hui Li, Jianfeng Ma, Qi Jiang & Ning Xi

  3. School of Information, Central University of Finance and Economics, Beijing, 102206, China

    Sheng Gao

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  1. Xindi Ma
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  2. Hui Li
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  3. Jianfeng Ma
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  6. Ning Xi
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  7. Di Lu
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Correspondence to Ning Xi.

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Ma, X., Li, H., Ma, J. et al. APPLET: a privacy-preserving framework for location-aware recommender system. Sci. China Inf. Sci. 60, 092101 (2017). https://doi.org/10.1007/s11432-015-0981-4

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