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Oh, H, Park, S, Lee, GM, Heo, H and Choi, JK (2019) Personal Data Trading Scheme for Data Brokers in IoT Data Marketplaces. IEEE Access, 7. pp. 40120-40132. ISSN 2169-3536
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Personal Data Trading Scheme for Data Brokers in IoT Data Marketplaces.pdf - Published Version Download (11MB) | Preview |
Abstract
With the widespread use of IoT, data-driven services take the lead of both online and offline businesses. Especially, personal data draw heavy attention to service providers because of 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 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 willingness-to-buy (WTB) of consumers. Therefore, this paper proposes an optimized trading model for data brokers which 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 the 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 data brokers spend costs to gather personal data.
| Item Type: | Article |
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| Uncontrolled Keywords: | data brokers; profit maximization; willingness-to-buy; willingness-to-sell |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Computer Science & Mathematics |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| Date Deposited: | 12 Mar 2019 12:22 |
| Last Modified: | 04 Sep 2021 09:38 |
| DOI or ID number: | 10.1109/ACCESS.2019.2904248 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/10298 |
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