Data providers' privacy valuation models for trustworthy personal data trading market

Abstract

With the widespread information and communication technology (ICT) environment, data-driven services take the lead of both online and offline businesses. Especially, personal data draw heavy attention to service providers because of its 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-party stakeholders. Since many services and applications utilize data analytic methods, 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 (mainly considered by business stakeholders) are revealed, data providers think the current ecosystem has risks and is untrustworthy due to little transparency and control of their personal data exploitation. With trustworthy methods to empower their rights of personal data, the data providers are interested in proper incentives in exchange for their privacy (i.e., privacy valuation) under their agreement. Therefore, this dissertation proposes data providers' privacy valuation models for trustworthy personal data trading market that consider data providers who weigh the value between privacy protection and valuation as well as other business stakeholders to cover various personal data value chains. Based on the realistic models for willingness-to-sell of data providers and willingness-to-buy (or -pay) of the business stakeholders, the feasibility is shown that the proposed models maximize the profits of the business stakeholders while satisfying all market participants even if they spend costs to gather personal data from the data providers. Moreover, this dissertation proposes the concept of trust provisioning that provides methods of minimizing risks through identifying the trust characteristics, which is used as useful information for decision making. By identifying both (socio-) technical aspects of the personal data ecosystem, it proposes the trust provisioning method for data providers to encourage their market participation.