Abstract
Since personal data becomes a valuable asset for IT industries, the necessity of data ecosystems with personal data trading methodologies is continuously increased, and the concept of data brokers is now widely used in the market. Moreover, utilizing the advantages of blockchain, distributed personal data markets have recently been considered because all participants behave selfishly for their profits in the markets. Since the participants may behave maliciously, it is necessary to prevent such behavior, and one of the considered approaches is putting additional deposits as a penalty into blockchain smart contracts. Since data brokers give various advantages to maintain personal data trading markets, many studies have considered both data brokers and blockchain at the same time. However, those studies have mainly focused on putting deposits into data buyers/sellers but not data brokers. However, since data brokers are also major players in the market, it is necessary to consider deposit decision models for data brokers. Hence, this paper proposes a deposit decision model for data brokers in distributed personal data markets using blockchain. Particularly, this paper proposes a profit model with deposits depending on their behavior for handling contracts and a credit level model that puts fewer deposits for a data broker with a higher credit level to motivate the data brokers’ truthful behavior. With the analysis of the proposed models, this paper shows that the models are feasible to motivate data brokers’ truthful behavior by allocating deposits for not only a large enough penalty but also a fair enough incentive.
Highlights
With emerging the importance of data, data-driven approaches are widely applied to both online and offline businesses by utilizing cutting edge technologies [1]
With the proposed models, this paper shows that the proposed deposit and credit level model can motivate data brokers to behave truthfully, and it proposes methods to decide a proper amount of deposits for data brokers in the personal data market
NUMERICAL RESULTS Based on the analysis for deciding deposit policies to manage incentive-compatible data trading model with data brokers in distributed personal data markets, this section shows various numerical results to check the feasibility of the proposed models
Summary
With emerging the importance of data, data-driven approaches are widely applied to both online and offline businesses by utilizing cutting edge technologies (including big data analysis, machine learning, artificial intelligence, etc.) [1]. As data-driven services and applications take the lead of both online and offline businesses, big data and the business analytic market size is projected to reach 512 Billion USD by 2026 with about 14% compound annual growth rate according to Valuates Reports [2]. Since data become valuable assets for IT industries, the necessity of data ecosystems with data trading methodologies is continuously increased. With these backgrounds, many reports investigated various big data ecosystems driven by data brokers [3]–[6].
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