Privacy Protection in Mobile Recommender Systems: A Survey

Kun Xu, Zheng Yan

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

8 Citations (Scopus)

Abstract

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
DOIs
Publication statusPublished - Nov 2016
MoE publication typeA4 Article in a 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
PublisherSpringer
Volume10066
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Security, Privacy and Anonymity in Computation, Communication and Storage
Abbreviated titleSpaCCS
CountryChina
CityZhangjiajie
Period16/11/201618/11/2016

Keywords

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

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