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Competitive Data Trading Model With Privacy Valuation for Multiple Stakeholders in IoT Data Markets
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- Authors
- Hyeontaek Oh, Sangdon Park, Gyu Myoung Lee, Jun Kyun Choi, Sungkee Noh
- Issue Date
- 2020-04
- Citation
- IEEE Internet of Things Journal, v.7, no.4, pp.3623-3639
- ISSN
- 2327-4662
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- Abstract
- With the widespread of Internet-of-Things (IoT) environment, a big data concept has emerged to handle a large number of data generated by IoT devices. Moreover, since data-driven approaches now become important for business, IoT data markets have emerged, and IoT big data are exploited by major stakeholders, such as data brokers and data service providers. Since many services and applications utilize data analytic methods with collected data from IoT devices, the conflict issues between privacy and data exploitation are raised, and the markets are mainly categorized as privacy protection markets and privacy valuation markets, respectively. Since these kinds of data value chains (which are mainly considered by business stakeholders) are revealed, data providers are interested in proper incentives in exchange for their privacy (i.e., privacy valuation) under their agreement. Therefore, this article proposes a competitive data trading model that consists of data providers who weigh the value between privacy protection and valuation as well as other business stakeholders. Each data broker considers the willingness-to-sell of data providers, and a single data service provider considers the willingness-to-pay of service consumers. At the same time, multiple data brokers compete to sell their data set to the data service provider as a noncooperative game model. Based on the Nash equilibrium analysis (NE) of the game, the feasibility is shown that the proposed model has the unique NE that maximizes the profits of business stakeholders while satisfying all market participants.
- KSP Keywords
- Analytic method, Big-data, Data Analytics, Data Providers, Data broker, Data sets, Data-driven approach, Equilibrium analysis, Game model, IoT devices, Market participants