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  • Differentially Private Collaborative Coupling Learning for Recommender Systems

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    Bai456563-Accepted.pdf (1.170Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Zhang, Y
    Bai, G
    Zhong, M
    Li, X
    Ko, R
    Griffith University Author(s)
    Bai, Guangdong
    Year published
    2020
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    Abstract
    IEEE Coupling learning is designed to estimate, discover and extract the interactions and relationships among learning components. It provides insights into complex interactive data, and has been extensively incorporated into recommender systems to enhance the interpretability of sophisticated relationships between users and items. Coupling learning can be further fostered once the trending collaborative learning can be engaged to take advantage of the cross-platform data. To facilitate this, privacy-preserving solutions are in high demandit is desired that the collaboration should not expose either the private data of each ...
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    IEEE Coupling learning is designed to estimate, discover and extract the interactions and relationships among learning components. It provides insights into complex interactive data, and has been extensively incorporated into recommender systems to enhance the interpretability of sophisticated relationships between users and items. Coupling learning can be further fostered once the trending collaborative learning can be engaged to take advantage of the cross-platform data. To facilitate this, privacy-preserving solutions are in high demandit is desired that the collaboration should not expose either the private data of each individual owner or the model parameters trained on their datasets. In this work, we develop a distributed collaborative coupling learning system which enables differential privacy. The proposed system defends against the adversary who has gained full knowledge of the training mechanism and the access to the model trained collaboratively. It also addresses the privacy-utility tradeoff by a provable tight sensitivity bound. Our experiments demonstrate that the proposed system guarantees favourable privacy gains at a modest cost in recommendation quality, even in scenarios with a large number of training epochs.
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    Journal Title
    IEEE Intelligent Systems
    DOI
    https://doi.org/10.1109/MIS.2020.3005930
    Copyright Statement
    © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Note
    This publication has been entered as an advanced online version in Griffith Research Online.
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/400599
    Collection
    • Journal articles

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