tutorial

Privacy for Recommender Systems: Tutorial Abstract

Authors:

Bart P. Knijnenburg,

Shlomo BerkovskyAuthors Info & Claims

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

Pages 394 - 395

https://doi.org/10.1145/3109859.3109935

Published: 27 August 2017 Publication History

Abstract

It is important for recommender system designers and service providers to learn about ways to generate accurate recommendations while at the same time respecting the privacy of their users. In this tutorial, we analyze common privacy risks imposed by recommender systems, survey privacy-enhanced recommendation techniques, and discuss implications for users.

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Cited By

Alzughbi HCaine KIqbal MKnijnenburg BLee HMcGregor SSidnam-Mauch E(2024)EPIC: Enhanced Privacy and Integrity Considerations for Research (Tutorial)Companion Proceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640544.3645249(166-168)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640544.3645249
Wu ZZhou XZhu YWang CHao G(2024)A Privacy Leakage Detection Method for Personalized Course Recommendation Based on Pi-Calculus2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C)10.1109/QRS-C63300.2024.00056(391-400)Online publication date: 1-Jul-2024
https://doi.org/10.1109/QRS-C63300.2024.00056
Cheng BChen PZhang XFang KQin XLiu W(2023)Personalized Privacy Protection-Preserving Collaborative Filtering Algorithm for Recommendation SystemsApplied Sciences10.3390/app1307460013:7(4600)Online publication date: 5-Apr-2023
https://doi.org/10.3390/app13074600
Show More Cited By

Index Terms

Privacy for Recommender Systems: Tutorial Abstract
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
2. Security and privacy

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Information & Contributors

Information

Published In

cover image ACM Conferences

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

August 2017

466 pages

ISBN:9781450346528

DOI:10.1145/3109859

General Chairs:
Paolo Cremonesi
Politecnico di Milano, Italy
,
Francesco Ricci
Free University Bozen-Bolzano, Italy
,
Program Chairs:
Shlomo Berkovsky
CSIRO, Australia
,
Alexander Tuzhilin
New York University, USA

Copyright © 2017 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 August 2017

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Tutorial

Conference

RecSys '17

Sponsor:

SIGCHI

RecSys '17: Eleventh ACM Conference on Recommender Systems

August 27 - 31, 2017

Como, Italy

Acceptance Rates

RecSys '17 Paper Acceptance Rate 26 of 125 submissions, 21%;

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

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Cited By

Alzughbi HCaine KIqbal MKnijnenburg BLee HMcGregor SSidnam-Mauch E(2024)EPIC: Enhanced Privacy and Integrity Considerations for Research (Tutorial)Companion Proceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640544.3645249(166-168)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640544.3645249
Wu ZZhou XZhu YWang CHao G(2024)A Privacy Leakage Detection Method for Personalized Course Recommendation Based on Pi-Calculus2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C)10.1109/QRS-C63300.2024.00056(391-400)Online publication date: 1-Jul-2024
https://doi.org/10.1109/QRS-C63300.2024.00056
Cheng BChen PZhang XFang KQin XLiu W(2023)Personalized Privacy Protection-Preserving Collaborative Filtering Algorithm for Recommendation SystemsApplied Sciences10.3390/app1307460013:7(4600)Online publication date: 5-Apr-2023
https://doi.org/10.3390/app13074600
Guo YLiu FZhou TCai ZXiao N(2023)Privacy Vs. Efficiency: Achieving Both Through Adaptive Hierarchical Federated LearningIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.3244198(1-12)Online publication date: 2023
https://doi.org/10.1109/TPDS.2023.3244198
Guo JZhao QLi GChen YLao CFeng L(2023)Decentralized federated learning with privacy-preserving for recommendation systemsEnterprise Information Systems10.1080/17517575.2023.219316317:9Online publication date: 28-Mar-2023
https://doi.org/10.1080/17517575.2023.2193163
Harasic MKeese FMattern DPaschke A(2023)Recent advances and future challenges in federated recommender systemsInternational Journal of Data Science and Analytics10.1007/s41060-023-00442-417:4(337-357)Online publication date: 25-Aug-2023
https://doi.org/10.1007/s41060-023-00442-4
Shakil Arif MSohail SAlam MUbaid SNafis MWang G(2022)Towards a Two-Tier Architecture for Privacy-Enabled Recommender Systems (PeRS)Ubiquitous Security10.1007/978-981-19-0468-4_20(268-278)Online publication date: 26-Feb-2022
https://doi.org/10.1007/978-981-19-0468-4_20
Abduljabbar TTao XZhang JZhou XLi LCai Y(2021)A Survey of Privacy Solutions using Blockchain for Recommender Systems: Current Status, Classification and Open IssuesThe Computer Journal10.1093/comjnl/bxab065Online publication date: 31-May-2021
https://doi.org/10.1093/comjnl/bxab065
Deldjoo YBellogin ADi Noia T(2021)Explaining recommender systems fairness and accuracy through the lens of data characteristicsInformation Processing & Management10.1016/j.ipm.2021.10266258:5(102662)Online publication date: Sep-2021
https://doi.org/10.1016/j.ipm.2021.102662
Raj SSahoo APradhan C(2020)Privacy Preserving in Collaborative Filtering Based Recommender System: A Systematic Literature ReviewProgress in Computing, Analytics and Networking10.1007/978-981-15-2414-1_52(513-522)Online publication date: 27-Mar-2020
https://doi.org/10.1007/978-981-15-2414-1_52
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