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Point-of-Interest Recommendation with User’s Privacy Preserving in an IoT Environment

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Abstract

With the popularization of smart devices and the rapid development of Internet of Things (IoT), location-based social networks (LBSNs) are growing rapidly. As a crucial personalized location service of LBSNs, point-of-interest (POI) recommendation has become a research hotspot. However, due to the use of personal information, POI recommendation system brings serious risks of privacy disclosure. Existing studies mainly focused on improving recommendation performance while ignoring privacy issues. To cope with the challenges, we propose a POI recommendation framework with users’ privacy preserving in an IoT environment based on local differential privacy (LDP). We first design an LDP-friendly POI recommendation method based on improved Hawkes process (HawkesRec) to integrate users’ long-term static and time-varying preferences. Then we put forward a privacy preserving recommendation framework based on HawkesRec and local differential privacy to protect the visited POIs and recommendation results of users. Experimental results over three real-world datasets demonstrate that the proposed solution achieves better performance than other baselines and has a good capability of privacy preserving.

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Notes

  1. https://tech.meituan.com/2014/09/05/lucene-distance.html

  2. http://snap.stanford.edu/data/loc-gowalla.html

  3. https://nj.meituan.com

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China under Grant No. 61672276, the National Key Research and Development Program of China under Grant No. 2017YFB1400600, Jiangsu Key Research and Development Program of China under Grant No. BE2019104, Jiangsu Natural Science Foundation of China under Grant No. BK20171037, and the Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing University.

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Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Guoming Zhang & Wanchun Dou

  2. School of Information Science and Engineering, Qufu Normal University, Qufu, China

    Lianyong Qi

  3. Department of Computing, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109, Australia

    Xuyun Zhang

  4. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China

    Xiaolong Xu

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  1. Guoming Zhang
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Correspondence to Wanchun Dou.

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Zhang, G., Qi, L., Zhang, X. et al. Point-of-Interest Recommendation with User’s Privacy Preserving in an IoT Environment. Mobile Netw Appl 26, 2445–2460 (2021). https://doi.org/10.1007/s11036-021-01784-8

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