research-article

BlurMe: inferring and obfuscating user gender based on ratings

Authors:

Stratis Ioannidis,

Nina TaftAuthors Info & Claims

RecSys '12: Proceedings of the sixth ACM conference on Recommender systems

Pages 195 - 202

https://doi.org/10.1145/2365952.2365989

Published: 09 September 2012 Publication History

Abstract

User demographics, such as age, gender and ethnicity, are routinely used for targeting content and advertising products to users. Similarly, recommender systems utilize user demographics for personalizing recommendations and overcoming the cold-start problem. Often, privacy-concerned users do not provide these details in their online profiles. In this work, we show that a recommender system can infer the gender of a user with high accuracy, based solely on the ratings provided by users (without additional metadata), and a relatively small number of users who share their demographics. Focusing on gender, we design techniques for effectively adding ratings to a user's profile for obfuscating the user's gender, while having an insignificant effect on the recommendations provided to that user.

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Cited By

Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/36528913:2(1-68)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3652891
Hu WFang H(2024)Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network ApproachACM Transactions on Knowledge Discovery from Data10.1145/3643821Online publication date: 30-Jan-2024
https://doi.org/10.1145/3643821
Shi TZhang YZhang JFeng FHe XHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization ApproachProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657822(448-457)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657822
Show More Cited By

Index Terms

BlurMe: inferring and obfuscating user gender based on ratings
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

RecSys '12: Proceedings of the sixth ACM conference on Recommender systems

September 2012

376 pages

ISBN:9781450312707

DOI:10.1145/2365952

General Chairs:
Pádraig Cunningham
University College Dublin, Ireland
,
Neil Hurley
University College Dublin, Ireland
,
Program Chairs:
Ido Guy
IBM Haifa Research Laboratory, Israel
,
Sarabjot Singh Anand
University of Warwick, England

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 September 2012

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RecSys '12

Sponsor:

SIGCHI

RecSys '12: Sixth ACM Conference on Recommender Systems

September 9 - 13, 2012

Dublin, Ireland

Acceptance Rates

RecSys '12 Paper Acceptance Rate 24 of 119 submissions, 20%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Citations

Cited By

Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/36528913:2(1-68)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3652891
Hu WFang H(2024)Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network ApproachACM Transactions on Knowledge Discovery from Data10.1145/3643821Online publication date: 30-Jan-2024
https://doi.org/10.1145/3643821
Shi TZhang YZhang JFeng FHe XHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization ApproachProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657822(448-457)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657822
Zhang SYuan WYin H(2024)Comprehensive Privacy Analysis on Federated Recommender System Against Attribute Inference AttacksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.329560136:3(987-999)Online publication date: Mar-2024
https://doi.org/10.1109/TKDE.2023.3295601
Ran XYe QHu HHuang XXu JFu J(2024)Differentially Private Graph Neural Networks for Link Prediction2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00133(1632-1644)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00133
Vassøy BLangseth H(2024)Consumer-side fairness in recommender systems: a systematic survey of methods and evaluationArtificial Intelligence Review10.1007/s10462-023-10663-557:4Online publication date: 29-Mar-2024
https://doi.org/10.1007/s10462-023-10663-5
Zheng YXiong TOuyang HWang SHua ZGao Y(2024)SARA: A Sparsity-Aware Efficient Oblivious Aggregation Service for Federated Matrix FactorizationWeb Information Systems Engineering – WISE 202410.1007/978-981-96-0567-5_17(227-242)Online publication date: 3-Dec-2024
https://doi.org/10.1007/978-981-96-0567-5_17
Escobedo GMoscati MMuellner PKopeinik SKowald DLex ESchedl M(2024)Making Alice Appear Like Bob: A Probabilistic Preference Obfuscation Method For Implicit Feedback Recommendation ModelsMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-031-70368-3_21(349-365)Online publication date: 22-Aug-2024
https://doi.org/10.1007/978-3-031-70368-3_21
Abreu MViegas ESantin A(2024)A Review of Social Network Regulations and Mechanisms for Safeguarding Children’s PrivacyAdvanced Information Networking and Applications10.1007/978-3-031-57931-8_41(427-438)Online publication date: 9-Apr-2024
https://doi.org/10.1007/978-3-031-57931-8_41
Müllner PLex ESchedl MKowald D(2024)The Impact of Differential Privacy on Recommendation Accuracy and Popularity BiasAdvances in Information Retrieval10.1007/978-3-031-56066-8_33(466-482)Online publication date: 15-Mar-2024
https://doi.org/10.1007/978-3-031-56066-8_33
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