Privacy Protection in Mobile Recommender Systems: A Survey

Kun Xu, Zheng Yan

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    15 Citations (Scopus)


    A Mobile Recommender System (MRS) is a system that provides personalized recommendations for mobile users. It solves the problem of information overload in a mobile environment with the support of a smart mobile device. MRS has three fundamental characteristics relevant to the mobile Internet: mobility, portability and wireless connectivity. MRS aims to generate accurate recommendations by utilizing detailed personal data and extracting user preferences. However, collecting and processing personal data may intrude user privacy. The privacy issues in MRS are more complex than traditional recommender system due to its specific characteristics and various personal data collection. Privacy protection in MRS is a crucial research topic, which is widely studied in the literature, but it still lacks a comprehensive survey to summarize its current status and indicate open research issues for further investigation. This paper reviews existing work in MRS in terms of privacy protection. Challenges and future research directions are discussed based on the literature survey.
    Original languageEnglish
    Title of host publicationSecurity, privacy, and anonymity in computation, communication, and storage
    Subtitle of host publication9th International Conference, SpaCCS 2016, Zhangjiajie, China, November 16-18, 2016, proceedings
    EditorsGuojun Wang, Indrakshi Ray, Jose M. Alcaraz Calero, Sabu M. Thampi
    Number of pages14
    ISBN (Electronic)978-3-319-49148-6
    ISBN (Print)978-3-319-49147-9
    Publication statusPublished - Nov 2016
    MoE publication typeA4 Conference publication
    EventInternational Conference on Security, Privacy and Anonymity in Computation, Communication and Storage - Zhangjiajie, China
    Duration: 16 Nov 201618 Nov 2016
    Conference number: 9

    Publication series

    NameLecture notes in computer science
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    ConferenceInternational Conference on Security, Privacy and Anonymity in Computation, Communication and Storage
    Abbreviated titleSpaCCS


    • Recommender systems
    • Mobile recommender systems
    • Privacy risks
    • Privacy protection
    • Mobile applications


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