research-article

Privacy-preserving Hybrid Recommender System

Authors:

Husen WangAuthors Info & Claims

SCC '17: Proceedings of the Fifth ACM International Workshop on Security in Cloud Computing

Pages 59 - 66

https://doi.org/10.1145/3055259.3055268

Published: 02 April 2017 Publication History

Abstract

Privacy issues in recommender systems have attracted the attention of researchers for many years. So far, a number of solutions have been proposed. Unfortunately, most of them are far from practical as they either downgrade the utility or are very inefficient. In this paper, we aim at a more practical solution, by proposing a privacy-preserving hybrid recommender system which consists of an incremental matrix factorization (IMF) component and a user-based collaborative filtering (UCF) component. The IMF component provides the fundamental utility while it allows the service provider to efficiently learn feature vectors in plaintext domain, and the UCF component improves the utility while allows users to carry out their computations in an offline manner. Leveraging somewhat homomorphic encryption (SWHE) schemes, we provide privacy-preserving candidate instantiations for both components. Our experiments demonstrate that the hybrid solution is much more efficient than existing solutions.

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Cited By

Ogunseyi TAvoussoukpo CJiang Y(2023)A systematic review of privacy techniques in recommendation systemsInternational Journal of Information Security10.1007/s10207-023-00710-122:6(1651-1664)Online publication date: 5-Jun-2023
https://doi.org/10.1007/s10207-023-00710-1
Vu-Thi VLuong-The D(2023)An Elliptic Curve-Based Privacy-Preserving Recommender SystemIntegrated Uncertainty in Knowledge Modelling and Decision Making10.1007/978-3-031-46781-3_28(334-345)Online publication date: 25-Oct-2023
https://doi.org/10.1007/978-3-031-46781-3_28
Ogunseyi TAvoussoukpo CJiang Y(2021)Privacy-Preserving Matrix Factorization for Cross-Domain RecommendationIEEE Access10.1109/ACCESS.2021.30914269(91027-91037)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3091426
Show More Cited By

Index Terms

Privacy-preserving Hybrid Recommender System
1. Security and privacy
  1. Security services
    1. Privacy-preserving protocols

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Published In

cover image ACM Conferences

SCC '17: Proceedings of the Fifth ACM International Workshop on Security in Cloud Computing

April 2017

100 pages

ISBN:9781450349703

DOI:10.1145/3055259

Program Chairs:
Cong Wang
City University of Hong Kong, China
,
Murat Kantarcioglu
University of Texas at Dallas, USA

Copyright © 2017 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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New York, NY, United States

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Published: 02 April 2017

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ASIA CCS '17

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SIGSAC

ASIA CCS '17: ACM Asia Conference on Computer and Communications Security

April 2, 2017

Abu Dhabi, United Arab Emirates

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SCC '17 Paper Acceptance Rate 11 of 27 submissions, 41%;

Overall Acceptance Rate 64 of 159 submissions, 40%

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Cited By

Ogunseyi TAvoussoukpo CJiang Y(2023)A systematic review of privacy techniques in recommendation systemsInternational Journal of Information Security10.1007/s10207-023-00710-122:6(1651-1664)Online publication date: 5-Jun-2023
https://doi.org/10.1007/s10207-023-00710-1
Vu-Thi VLuong-The D(2023)An Elliptic Curve-Based Privacy-Preserving Recommender SystemIntegrated Uncertainty in Knowledge Modelling and Decision Making10.1007/978-3-031-46781-3_28(334-345)Online publication date: 25-Oct-2023
https://doi.org/10.1007/978-3-031-46781-3_28
Ogunseyi TAvoussoukpo CJiang Y(2021)Privacy-Preserving Matrix Factorization for Cross-Domain RecommendationIEEE Access10.1109/ACCESS.2021.30914269(91027-91037)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3091426
Himeur YAlsalemi AAl-Kababji ABensaali FAmira ASardianos CDimitrakopoulos GVarlamis I(2021)A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospectsInformation Fusion10.1016/j.inffus.2021.02.002Online publication date: Feb-2021
https://doi.org/10.1016/j.inffus.2021.02.002

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