Personal big data pricing method based on differential privacy

被引：15
|
作者：
Shen, Yuncheng [1 ,2 ]
Guo, Bing [1 ]
Shen, Yan [3 ]
Duan, Xuliang [1 ]
Dong, Xiangqian [1 ]
Zhang, Hong [1 ]
Zhang, Chuanwu [4 ]
Jiang, Yuming [1 ]
机构：
[1] Sichuan Univ, Coll Comp Sci, Chengdu 610065, Peoples R China
[2] Zhaotong Univ, Coll Informat Sci & Technol, Zhaotong 657000, Peoples R China
[3] Chengdu Univ Informat Technol, Sch Comp Sci, Chengdu 610054, Peoples R China
[4] Southwest Minzu Univ, Coll Elect & Informat Engn, Chengdu 610041, Peoples R China
来源：
COMPUTERS & SECURITY | 2022年 / 113卷
基金：
中国国家自然科学基金;
关键词：
Personal big data; Data privacy; Privacy protection; Differential privacy; Positive pricing; Reverse pricing; Privacy budget; Privacy compensation; VIEWS;
D O I：
10.1016/j.cose.2021.102529
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Personal big data can greatly promote social management, business applications, and personal services, and bring certain economic benefits to users. The difficulty with personal big data security and privacy protection lies in realizing the maximization of the value of personal big data and in striking a balance between data privacy protection and sharing on the premise of satisfying personal big data security and privacy protection. Thus, in this paper, we propose a personal big data p ricing m ethod based on d ifferential p rivacy (PMDP). We design two different mechanisms of positive and reverse pricing to reasonbly price personal big data. We perform aggregate statistics on an open dataset and extensively evaluated its performance. The experimental results show that PMDP can provide reasonable pricing for personal big data and fair compensation to data owners, ensuring an arbitrage-free condition and finding a balance between privacy protection and data utility. (c) 2021 Elsevier Ltd. All rights reserved.
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收藏
页数：18
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