Paper Info
Title
Personal Data Trading Scheme for Data Brokers in IoT Data Marketplaces.
Abstract
With the widespread use of the Internet of Things, data-driven services take the lead of both online and off-line businesses. Especially, personal data draw heavy attention of service providers because of the usefulness in value-added services. With the emerging big-data technology, a data broker appears, which exploits and sells personal data about individuals to other third parties. Due to little transparency between providers and brokers/consumers, people think that the current ecosystem is not trustworthy, and new regulations with strengthening the rights of individuals were introduced. Therefore, people have an interest in their privacy valuation. In this sense, the willingness-to-sell (WTS) of providers becomes one of the important aspects for data brokers; however, conventional studies have mainly focused on the willingnessto-buy (WTB) of consumers. Therefore, this paper proposes an optimized trading model for data brokers who buy personal data with proper incentives based on the WTS, and they sell valuable information from the refined dataset by considering the WTB and the dataset quality. This paper shows that the proposed model has a global optimal point by the convex optimization technique and proposes a gradient ascent-based algorithm. Consequently, it shows that the proposed model is feasible even if the data brokers spend costs to gather personal data.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2904248
IEEE ACCESS
Keywords
Field
DocType
Data brokers,profit maximization,willingness-to-buy,willingness-to-sell
Transparency (graphic),Gradient descent,Incentive,Computer security,Computer science,Internet of Things,Computer network,Exploit,Service provider,Valuation (finance),Convex optimization
Journal
Volume
ISSN
Citations 
7
2169-3536
3
PageRank 
References 
Authors
0.42
0
5
Name
Order
Citations
PageRank
hyeontaek oh1158.66
Sangdon Park2399.11
Gyu Myoung Lee331237.63
Hwanjo Heo430.42
Jun-Kyun Choi517543.94