research-article

Privacy-preserving content-based recommender system

Authors:

Zekeriya Erkin,

Reginald L. LagendijkAuthors Info & Claims

MM&Sec '12: Proceedings of the on Multimedia and security

Pages 77 - 84

https://doi.org/10.1145/2361407.2361420

Published: 06 September 2012 Publication History

Abstract

By offering personalized content to users, recommender systems have become a vital tool in e-commerce and online media applications. Content-based algorithms recommend items or products to users, that are most similar to those previously purchased or consumed. Unfortunately, collecting and storing ratings, on which content-based methods rely, also poses a serious privacy risk for the customers: ratings may be very personal or revealing, and thus highly privacy sensitive. Service providers could process the collected rating data for other purposes, sell them to third parties or fail to provide adequate physical security. In this paper, we propose technological mechanisms to protect the privacy of individuals in a recommender system. Our proposal is founded on homomorphic encryption, which is used to obscure the private rating information of the customers from the service provider. While the user's privacy is respected by the service provider, by generating recommendations using encrypted customer ratings, the service provider's commercially valuable item-item similarities are protected against curious entities, in turn. Our proposal explores simple and efficient cryptographic techniques to generate private recommendations using a server-client model, which neither relies on (trusted) third parties, nor requires interaction with peer users. The main strength of our contribution lies in providing a highly efficient solution without resorting to unrealistic assumptions.

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Cited By

Hu WFang H(2024)Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network ApproachACM Transactions on Knowledge Discovery from Data10.1145/364382118:5(1-21)Online publication date: 30-Jan-2024
https://dl.acm.org/doi/10.1145/3643821
Wu WWang JZhang YLiu ZZhou LLin X(2023)VPiP: Values Packing in Paillier for Communication Efficient Oblivious Linear ComputationsIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.329048318(4214-4228)Online publication date: 2023
https://doi.org/10.1109/TIFS.2023.3290483
Cui LWang X(2022)A Cascade Framework for Privacy-Preserving Point-of-Interest Recommender SystemElectronics10.3390/electronics1107115311:7(1153)Online publication date: 6-Apr-2022
https://doi.org/10.3390/electronics11071153
Show More Cited By

Index Terms

Privacy-preserving content-based recommender system
1. Security and privacy
  1. Cryptography
    1. Public key (asymmetric) techniques
      1. Public key encryption

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Published In

cover image ACM Conferences

MM&Sec '12: Proceedings of the on Multimedia and security

September 2012

184 pages

ISBN:9781450314176

DOI:10.1145/2361407

General Chair:
Chang-Tsun Li
University of Warwick, UK
,
Program Chairs:
Jana Dittmann
Otto-von-Guericke University, Germany
,
Stefan Katzenbeisser
Technische Universität Darmstadt, Germany
,
Scott Carver
SUNY Binghamton, USA

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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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 September 2012

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MM&Sec '12

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SIGMM

MM&Sec '12: Multimedia and Security Workshop

September 6 - 7, 2012

Coventry, United Kingdom

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Overall Acceptance Rate 128 of 318 submissions, 40%

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Citations

Cited By

Hu WFang H(2024)Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network ApproachACM Transactions on Knowledge Discovery from Data10.1145/364382118:5(1-21)Online publication date: 30-Jan-2024
https://dl.acm.org/doi/10.1145/3643821
Wu WWang JZhang YLiu ZZhou LLin X(2023)VPiP: Values Packing in Paillier for Communication Efficient Oblivious Linear ComputationsIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.329048318(4214-4228)Online publication date: 2023
https://doi.org/10.1109/TIFS.2023.3290483
Cui LWang X(2022)A Cascade Framework for Privacy-Preserving Point-of-Interest Recommender SystemElectronics10.3390/electronics1107115311:7(1153)Online publication date: 6-Apr-2022
https://doi.org/10.3390/electronics11071153
Chandra AAhmed AKumar SChand PBorah MHussain Z(2022)Content-Based Recommender System for Similar Products in E-CommerceEdge Analytics10.1007/978-981-19-0019-8_46(617-628)Online publication date: 4-Apr-2022
https://doi.org/10.1007/978-981-19-0019-8_46
Sangeetha SSudha Sadasivam GNithesh VMounish K(2022)Confluence of Cryptography and Differential Privacy: A Hybrid Approach for Privacy Preserving Collaborative FilteringProceedings of the International Conference on Paradigms of Communication, Computing and Data Sciences10.1007/978-981-16-5747-4_29(333-345)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-981-16-5747-4_29
Ahmad AAhmad TTripathi I(2021)Identifying Attack Models for Securing Cluster-based Recommendation SystemRecent Patents on Engineering10.2174/187221211466620040309105314:3(324-338)Online publication date: 19-Jan-2021
https://doi.org/10.2174/1872212114666200403091053
Masood RBerkovsky SKaafar M(2021)Tracking and PersonalizationModern Socio-Technical Perspectives on Privacy10.1007/978-3-030-82786-1_9(171-202)Online publication date: 29-Jul-2021
https://doi.org/10.1007/978-3-030-82786-1_9
Lekshmi Priya TSandhya H(2020)Matrix Factorization for Recommendation SystemAdvances in Artificial Intelligence and Data Engineering10.1007/978-981-15-3514-7_22(267-280)Online publication date: 14-Aug-2020
https://doi.org/10.1007/978-981-15-3514-7_22
Jain MSingh SChandrasekaran K(2018)A Systematic Mapping Study of Content Based Filtering Recommender SystemsInternational Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 201810.1007/978-3-030-03146-6_30(273-282)Online publication date: 21-Dec-2018
https://doi.org/10.1007/978-3-030-03146-6_30
Ogunseyi TYang C(2018)Survey and Analysis of Cryptographic Techniques for Privacy Protection in Recommender SystemsCloud Computing and Security10.1007/978-3-030-00012-7_63(691-706)Online publication date: 13-Sep-2018
https://doi.org/10.1007/978-3-030-00012-7_63
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