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View all- Chen CWang KLi PSakurai K(2024)Enhancing Security and Efficiency: A Lightweight Federated Learning ApproachAdvanced Information Networking and Applications10.1007/978-3-031-57916-5_30(349-359)Online publication date: 9-Apr-2024
Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation
Authors: 
Zeyu Cao, Zhipeng Liang, Bingzhe Wu, Shu Zhang, Hangyu Li, Ouyang Wen, Yu Rong, Peilin ZhaoAuthors Info & Claims
Zeyu Cao, Zhipeng Liang, Bingzhe Wu, Shu Zhang, Hangyu Li, Ouyang Wen, Yu Rong, Peilin ZhaoAuthors Info & ClaimsPages 154 - 166
Abstract
Recent awareness of privacy protection and compliance requirement resulted in a controversial view of recommendation system due to personal data usage. Therefore, privacy-protected recommendation emerges as a novel research direction. In this paper, we first formulate this problem as a vertical federated learning problem, i.e., features are vertically distributed over different departments. We study a contextual bandit learning problem for recommendation in the vertical federated setting. To this end, we carefully design a customized encryption scheme named orthogonal matrix-based mask mechanism (O3M). O3M mechanism, a tailored component for contextual bandits by carefully exploiting their shared structure, can ensure privacy protection while avoiding expensive conventional cryptographic techniques. We further apply the mechanism to two commonly-used bandit algorithms, LinUCB and LinTS, and instantiate two practical protocols for online recommendation. The proposed protocols can perfectly recover the service quality of centralized bandit algorithms while achieving a satisfactory runtime efficiency, which is theoretically proved and analysed in this paper. By conducting extensive experiments on both synthetic and real-world datasets, we show the superiority of the proposed method in terms of privacy protection and recommendation performance.
Supplementary Material
We propose O3M, which can be plugged into most of the linear contextual bandits algorithms to achieve the privacy protection utility.
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Index Terms
- Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation
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August 2023
5996 pages
ISBN:9798400701030
DOI:10.1145/3580305
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Published: 04 August 2023
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View all- Chen CWang KLi PSakurai K(2024)Enhancing Security and Efficiency: A Lightweight Federated Learning ApproachAdvanced Information Networking and Applications10.1007/978-3-031-57916-5_30(349-359)Online publication date: 9-Apr-2024
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