Privacy Protection in Mobile Recommender Systems: A Survey

Kun Xu; Zheng Yan

doi:10.1007/978-3-319-49148-6_26

Privacy Protection in Mobile Recommender Systems: A Survey

Kun Xu, Zheng Yan

Xidian University

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference contribution › Scientific › vertaisarvioitu

14 Sitaatiot (Scopus)

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	Security, privacy, and anonymity in computation, communication, and storage
Alaotsikko	9th International Conference, SpaCCS 2016, Zhangjiajie, China, November 16-18, 2016, proceedings
Toimittajat	Guojun Wang, Indrakshi Ray, Jose M. Alcaraz Calero, Sabu M. Thampi
Kustantaja	SPRINGER
Sivumäärä	14
ISBN (elektroninen)	978-3-319-49148-6
ISBN (painettu)	978-3-319-49147-9
DOI - pysyväislinkit	https://doi.org/10.1007/978-3-319-49148-6_26
Tila	Julkaistu - marrask. 2016
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisuussa
Tapahtuma	International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage - Zhangjiajie, Kiina Kesto: 16 marrask. 2016 → 18 marrask. 2016 Konferenssinumero: 9

Julkaisusarja

Nimi	Lecture notes in computer science
Kustantaja	Springer
Vuosikerta	10066
ISSN (painettu)	0302-9743
ISSN (elektroninen)	1611-3349

Conference

Conference	International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage
Lyhennettä	SpaCCS
Maa/Alue	Kiina
Kaupunki	Zhangjiajie
Ajanjakso	16/11/2016 → 18/11/2016

Pääsy asiakirjaan

10.1007/978-3-319-49148-6_26

OpenUrl-saatavuus

Koko teksti

Siteeraa tätä

Xu, K., & Yan, Z. (2016). Privacy Protection in Mobile Recommender Systems: A Survey. teoksessa G. Wang, I. Ray, J. M. Alcaraz Calero, & S. M. Thampi (Toimittajat), Security, privacy, and anonymity in computation, communication, and storage: 9th International Conference, SpaCCS 2016, Zhangjiajie, China, November 16-18, 2016, proceedings (Lecture notes in computer science; Vuosikerta 10066). SPRINGER. https://doi.org/10.1007/978-3-319-49148-6_26

Xu, Kun ; Yan, Zheng. / Privacy Protection in Mobile Recommender Systems : A Survey. Security, privacy, and anonymity in computation, communication, and storage: 9th International Conference, SpaCCS 2016, Zhangjiajie, China, November 16-18, 2016, proceedings. Toimittaja / Guojun Wang ; Indrakshi Ray ; Jose M. Alcaraz Calero ; Sabu M. Thampi. SPRINGER, 2016. (Lecture notes in computer science).

@inproceedings{b00a1e418b0148e3968ddcbe2d1d4376,

title = "Privacy Protection in Mobile Recommender Systems: A Survey",

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.",

keywords = "Recommender systems, Mobile recommender systems, Privacy risks, Privacy protection, Mobile applications",

author = "Kun Xu and Zheng Yan",

year = "2016",

month = nov,

doi = "10.1007/978-3-319-49148-6_26",

language = "English",

isbn = "978-3-319-49147-9",

series = "Lecture notes in computer science",

publisher = "SPRINGER",

editor = "Wang, {Guojun } and Ray, {Indrakshi } and {Alcaraz Calero}, {Jose M.} and Thampi, {Sabu M.}",

booktitle = "Security, privacy, and anonymity in computation, communication, and storage",

address = "Germany",

note = "International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, SpaCCS ; Conference date: 16-11-2016 Through 18-11-2016",

}

Xu, K & Yan, Z 2016, Privacy Protection in Mobile Recommender Systems: A Survey. julkaisussa G Wang, I Ray, JM Alcaraz Calero & SM Thampi (toim), Security, privacy, and anonymity in computation, communication, and storage: 9th International Conference, SpaCCS 2016, Zhangjiajie, China, November 16-18, 2016, proceedings. Lecture notes in computer science, Vuosikerta 10066, SPRINGER, International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, Zhangjiajie, Kiina, 16/11/2016. https://doi.org/10.1007/978-3-319-49148-6_26

Privacy Protection in Mobile Recommender Systems: A Survey. / Xu, Kun; Yan, Zheng.
Security, privacy, and anonymity in computation, communication, and storage: 9th International Conference, SpaCCS 2016, Zhangjiajie, China, November 16-18, 2016, proceedings. toim. / Guojun Wang; Indrakshi Ray; Jose M. Alcaraz Calero; Sabu M. Thampi. SPRINGER, 2016. (Lecture notes in computer science; Vuosikerta 10066).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference contribution › Scientific › vertaisarvioitu

TY - GEN

T1 - Privacy Protection in Mobile Recommender Systems

T2 - International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage

AU - Xu, Kun

AU - Yan, Zheng

N1 - Conference code: 9

PY - 2016/11

Y1 - 2016/11

N2 - 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.

AB - 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.

KW - Recommender systems

KW - Mobile recommender systems

KW - Privacy risks

KW - Privacy protection

KW - Mobile applications

U2 - 10.1007/978-3-319-49148-6_26

DO - 10.1007/978-3-319-49148-6_26

M3 - Conference contribution

SN - 978-3-319-49147-9

T3 - Lecture notes in computer science

BT - Security, privacy, and anonymity in computation, communication, and storage

A2 - Wang, Guojun

A2 - Ray, Indrakshi

A2 - Alcaraz Calero, Jose M.

A2 - Thampi, Sabu M.

PB - SPRINGER

Y2 - 16 November 2016 through 18 November 2016

ER -

Xu K, Yan Z. Privacy Protection in Mobile Recommender Systems: A Survey. julkaisussa Wang G, Ray I, Alcaraz Calero JM, Thampi SM, toimittajat, Security, privacy, and anonymity in computation, communication, and storage: 9th International Conference, SpaCCS 2016, Zhangjiajie, China, November 16-18, 2016, proceedings. SPRINGER. 2016. (Lecture notes in computer science). doi: 10.1007/978-3-319-49148-6_26

Privacy Protection in Mobile Recommender Systems: A Survey

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