research-article

Privacy Risks in Recommender Systems

Authors:

George KarypisAuthors Info & Claims

IEEE Internet Computing, Volume 5, Issue 6

Pages 54 - 62

https://doi.org/10.1109/4236.968832

Published: 01 November 2001 Publication History

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Abstract

The authors explore the conflict between personalization and privacy that arises from the existence of straddlers - users with eclectic tastes who rates products across several different types or domains -- in recommender systems. While straddlers enable serendipitous recommendations, information about their existence could be used in conjunction with other data sources to uncover identities and reveal personal details. This article discusses a graph theoretic model for studying the benefit for and risk to straddlers.

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Cited By

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Riyana SRiyana N(2024)Ensuring Security and Privacy Preservation for the Publication of Rating DatasetsSN Computer Science10.1007/s42979-024-02690-y5:4Online publication date: 27-Mar-2024
https://dl.acm.org/doi/10.1007/s42979-024-02690-y
Assenmacher DWeber DPreuss MCalero Valdez ABradshaw ARoss BCresci STrautmann HNeumann FGrimme C(2022)Benchmarking Crisis in Social Media AnalyticsSocial Science Computer Review10.1177/0894439321101226840:6(1496-1522)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1177/08944393211012268
Beigi GLiu H(2020)A Survey on Privacy in Social MediaACM/IMS Transactions on Data Science10.1145/33430381:1(1-38)Online publication date: 12-Mar-2020
https://dl.acm.org/doi/10.1145/3343038
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Published In

IEEE Internet Computing Volume 5, Issue 6

November 2001

97 pages

ISSN:1089-7801

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IEEE Educational Activities Department

United States

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Published: 01 November 2001

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Riyana SRiyana N(2024)Ensuring Security and Privacy Preservation for the Publication of Rating DatasetsSN Computer Science10.1007/s42979-024-02690-y5:4Online publication date: 27-Mar-2024
https://dl.acm.org/doi/10.1007/s42979-024-02690-y
Assenmacher DWeber DPreuss MCalero Valdez ABradshaw ARoss BCresci STrautmann HNeumann FGrimme C(2022)Benchmarking Crisis in Social Media AnalyticsSocial Science Computer Review10.1177/0894439321101226840:6(1496-1522)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1177/08944393211012268
Beigi GLiu H(2020)A Survey on Privacy in Social MediaACM/IMS Transactions on Data Science10.1145/33430381:1(1-38)Online publication date: 12-Mar-2020
https://dl.acm.org/doi/10.1145/3343038
Beigi GMosallanezhad AGuo RAlvari HNou ALiu HCaverlee JHu XLalmas MWang W(2020)Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial LearningProceedings of the 13th International Conference on Web Search and Data Mining10.1145/3336191.3371832(34-42)Online publication date: 20-Jan-2020
https://dl.acm.org/doi/10.1145/3336191.3371832
Riyana SNanthachumphu SRiyana N(2020)Achieving Privacy Preservation Constraints in Missing-Value DatasetsSN Computer Science10.1007/s42979-020-00241-91:4Online publication date: 4-Jul-2020
https://dl.acm.org/doi/10.1007/s42979-020-00241-9
Champiri ZFisher BFreund L(2020)rScholar: An Interactive Contextual User Interface to Enhance UX of Scholarly Recommender SystemsHCI International 2020 - Late Breaking Papers: User Experience Design and Case Studies10.1007/978-3-030-60114-0_43(662-686)Online publication date: 19-Jul-2020
https://dl.acm.org/doi/10.1007/978-3-030-60114-0_43
Wang JTang QArriaga ARyan P(2019)Novel collaborative filtering recommender friendly to privacy protectionProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367471.3367712(4809-4815)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367471.3367712
Liu CChen ZCao DShang M(2019)Application of Recommender System in Intelligent Community under Big Data ScenarioProceedings of the 2nd International Conference on Big Data Technologies10.1145/3358528.3359551(92-96)Online publication date: 28-Aug-2019
https://dl.acm.org/doi/10.1145/3358528.3359551
van Capelleveen GAmrit CYazan DZijm H(2019)The recommender canvasExpert Systems with Applications: An International Journal10.1016/j.eswa.2019.04.001129:C(97-117)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1016/j.eswa.2019.04.001
Kim JKoo DKim YYoon HShin JKim S(2018)Efficient Privacy-Preserving Matrix Factorization for Recommendation via Fully Homomorphic EncryptionACM Transactions on Privacy and Security10.1145/321250921:4(1-30)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3212509
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Acquiring User Information Needs for Recommender Systems

Emerging Paradigms for Privacy Preserving Recommender Systems

Exploring privacy concerns in news recommender systems

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