Paper Info
Title
A Privacy-Preserving Oriented Service Recommendation Approach based on Personal Data Cloud and Federated Learning
Abstract
Personal data cloud, as an emerging personal data management mode in recent years, enables to reduce the risk of privacy disclosure and protect the rights and interests of individuals given by privacy protection laws and regulations. Personal data that is generated during the interaction between individual users and various services contains a lot of useful personalized but private information and plays a crucial part in personalized service recommendation. In traditional service recommendation scenario, personal data of massive users is centralized owned/managed by service providers, which is easy to lead to privacy disclosure and personal data abuse. In the personal data cloud based service recommendation scenario, personal data of individual users is distributed stored and controlled by users themselves. To address the challenges of privacy protection and distributed storage of personalized data in this new recommendation scenario, we propose HyFL, a deep learning based recommendation algorithm with hybrid federated learning. HyFL can conduct recommendation based on the personal data from multiple services. The security of HyFL is theoretically proved, and experiments on real-world datasets demonstrate that HyFL performs better on the basis of privacy preservation than that of some traditional recommendation approaches.
Year
DOI
Venue
2022
10.1109/ICWS55610.2022.00054
2022 IEEE International Conference on Web Services (ICWS)
Keywords
DocType
ISBN
Privacy Preserving,Service Recommendation,Personal Data Cloud,Social Linked Data,Federated Learning
Conference
978-1-6654-8144-1
Citations 
PageRank 
References 
0
0.34
16
Authors
5
Name
Order
Citations
PageRank
Haochen Yuan100.68
Chao Ma28527.49
Zhenxiang Zhao300.68
Xu Xiaofei498770.10
Zhong-Jie Wang535664.60