User Friendly Preferential Private Recommendation

doi:10.19682/j.cnki.1005-8885.2022.1006

中国邮电高校学报(英文版) ›› 2022, Vol. 29 ›› Issue (3): 43-53.doi: 10.19682/j.cnki.1005-8885.2022.1006

User Friendly Preferential Private Recommendation

1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
2. China Unicom Network Technology Research Institute, Beijing 100044, China

收稿日期:2021-04-06 修回日期:2021-09-25 出版日期:2022-06-30 发布日期:2022-06-30
通讯作者: Yang Jingjing E-mail:19111044@bjtu.edu.cn
基金资助:
This work was supported by the National Natural Science Foundation of China (61872031).

User Friendly Preferential Private Recommendation

1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
2. China Unicom Network Technology Research Institute, Beijing 100044, China

Received:2021-04-06 Revised:2021-09-25 Online:2022-06-30 Published:2022-06-30
Contact: Yang Jingjing E-mail:19111044@bjtu.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (61872031).

摘要/Abstract

摘要：

To provide preferential protection for users while keeping good service utility, a preferential private recommendation framework ( named as PrefER) is proposed. In this framework, a preferential budget allocation scheme is designed and implemented at the system side to provide multilevel protection. And users' preference is utilized at the user side to improve recommendation performance without increasing users' burden. This framework is generic enough to be employed with other schemes. Recommendation accuracy based on the MovieLens dataset by the collaborative filtering schemes and PrefER are compared and analyzed. The experimental results show that PrefER can provide preferential privacy protection with the improvement of recommendation accuracy.

关键词: preferential privacy protection, differential privacy, top-k recommendation, personalized service

Abstract: To provide preferential protection for users while keeping good service utility, a preferential private recommendation framework ( named as PrefER) is proposed. In this framework, a preferential budget allocation scheme is designed and implemented at the system side to provide multilevel protection. And users' preference is utilized at the user side to improve recommendation performance without increasing users' burden. This framework is generic enough to be employed with other schemes. Recommendation accuracy based on the MovieLens dataset by the collaborative filtering schemes and PrefER are compared and analyzed. The experimental results show that PrefER can provide preferential privacy protection with the improvement of recommendation accuracy.

Key words: preferential privacy protection, differential privacy, top-k recommendation, personalized service

Yang Jingjing, Guo Yuchun, Feng Tingting, Chen Yishuai. User Friendly Preferential Private Recommendation[J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(3): 43-53.

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