"A Pricing Model for Big Personal Data" by Yuncheng Shen, Bing Guo et al.

Article Title

Authors

Yuncheng Shen, College of Computer Science, Sichuan University, Chengdu 610065, China.
Bing Guo, College of Computer Science, Sichuan University, Chengdu 610065, China.
Yan Shen, School of Control Engineering, Chengdu University of Information Technology, Chengdu 610225, China.
Xuliang Duan, College of Computer Science, Sichuan University, Chengdu 610065, China.
Xiangqian Dong, College of Computer Science, Sichuan University, Chengdu 610065, China.
Hong Zhang, College of Computer Science, Sichuan University, Chengdu 610065, China.

Keywords

data tuple, Big Personal Data, positive grading, reverse pricing, pricing model

Abstract

Big Personal Data is growing explosively. Consequently, an increasing number of internet users are drowning in a sea of data. Big Personal Data has enormous commercial value; it is a new kind of data asset. An urgent problem has thus arisen in the data market: How to price Big Personal Data fairly and reasonably. This paper proposes a pricing model for Big Personal Data based on tuple granularity, with the help of comparative analysis of existing data pricing models and strategies. This model is put forward to implement positive rating and reverse pricing for Big Personal Data by investigating data attributes that affect data value, and analyzing how the value of data tuples varies with information entropy, weight value, data reference index, cost, and other factors. The model can be adjusted dynamically according to these parameters. With increases in data scale, reductions in its cost, and improvements in its quality, Big Personal Data users can thereby obtain greater benefits.