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Open Access

Lightweight and Privacy-Preserving IoT Service Recommendation Based on Learning to Hash

School of Computer Science and Technology, Qufu Normal University, Rizhao 276800, China
Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Shouguang 262700, China
School of Information Engineering, China University of Geosciences, Beijing 100083, China
Department of Computing, Macquarie University, Sydney 2109, Australia
School of Information Engineering, Huzhou University, Huzhou 313000, China
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Abstract

In the Internet of Things (IoT) environment, user-service interaction data are often stored in multiple distributed platforms. In this situation, recommender systems need to integrate the distributed user-service interaction data across different platforms for making a comprehensive recommendation decision, during which user privacy is probably disclosed. Moreover, as user-service interaction records accumulate over time, they significantly reduce the efficiency of recommendations. To tackle these issues, we propose a lightweight and privacy-preserving service recommendation approach named SerRecL2H. In SerRecL2H, we employ Learning to Hash (L2H) to encapsulate sensitive user-service interaction data into less-sensitive user indices, which facilitates identifying users with similar preferences efficiently for accurate recommendations. We then validate the feasibility of our proposed SerRecL2H approach through massive experiments conducted on the popular WS-DREAM dataset. The comparative analysis with other competitive approaches demonstrates that our proposal surpasses other approaches in terms ofrecommendation accuracy and efficiency while protecting user privacy.

Keywords

service recommendationIoTprivacy protectionlearning to hashlightweight

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Tsinghua Science and Technology
Volume 30 Issue 4,
August 2025
Pages 1793-1807
DOI: 10.26599/TST.2024.9010064
Cite this article:
Wan H, Wu Y, Yang Y, et al. Lightweight and Privacy-Preserving IoT Service Recommendation Based on Learning to Hash. Tsinghua Science and Technology, 2025, 30(4): 1793-1807. https://doi.org/10.26599/TST.2024.9010064

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Received: 07 February 2024
Revised: 15 March 2024
Accepted: 24 March 2024
Published: 03 March 2025
© The Author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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