A Pricing Model for Big Personal Data

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 a...

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Bibliographic Details
Published in:Tsinghua science and technology 2016-10, Vol.21 (5), p.482-490
Main Author: Yuncheng Shen Bing Guo Yan Shen Xuliang Duan Xiangqian Dong Hong Zhang
Format: Article
Language:English
Subjects:
data
Data
positive
grading
reverse
Personal
pricing
pricing
tuple
Big
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Summary: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.
ISSN:1007-0214
1878-7606
1007-0214
DOI:10.1109/TST.2016.7590317