Abstract
The data market concept has gained a lot of momentum in recent years, fuelled by initiatives to set up such markets, e.g., on the European level. Still, the typical data market concept aims at providing a centralised platform with all of its positive and negative side effects. Internal data markets, also called local or on-premise data markets, on the other hand, are set up to allow data trade inside an institution (e.g., between divisions of a large company) or between members of a small, well-defined consortium, thus allowing the remuneration of providing data inside these structures. Still, while research on securing global data markets has garnered some attention throughout recent years, the internal data markets have been treated as being more or less similar in this respect. In this paper, we outline the major differences between global and internal data markets with respect to security and why further research is required. Furthermore, we provide a fundamental model for a secure internal data market that can be used as a starting point for the generation of concrete internal data market models. Finally, we provide an overview on the research questions we deem most pressing in order to make the internal data market concept work securely, thus allowing for more widespread adoption.
Highlights
We have provided an analysis of security issues in, as well as a basic model for, secure internal data markets
To the best of our knowledge, this is the first time global and local data markets were compared with respect to security in a structured way
Based on the results of this analysis, we see ample reasons for future research in this area, as most data markets concepts currently available typically focus on global approaches, even in cases where there is a limited number of foreseen partners
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The most obvious of these is that the entire market does not run internally on company systems, but is designed as an external platform This is absolutely unacceptable in many industries, especially in the field of medical products, and in the banking sector or in the processing of personal data. In the novel basic model provided in this paper, we provide a much more abstract view on the topic of internal data markets that allows specific tailoring to the requirements of the companies in question. This can range from security- and privacy-relevant aspects, such as data anonymisation stages, to issues of whether to charge money for data transfers, to resort to a karma point system, or to even not charge anything. A set of open research questions that need consideration in order to make internal data markets work securely in environments with limited resources
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