# Secure Internal Data Markets
## 2 Background and Related Work  
The collection and use of data have grown in popularity in recent years. While in the past, technical data were typically collected in the context of decision-making processes, e.g., in the context of predictive maintenance [3], in recent years, personal data have been increasingly used, especially in the area of marketing. A few large companies in particular control the entire market--above all, Google (or Alphabet). In recent years, there have been some initiatives to enable small- and medium-sized companies to use their data efficiently, especially to enable new products and services. In the health care sector, there are a number of approaches (e.g., dedicated research servers as in [4]) to make meaningful use of the data pools, whereby the involvement of external experts with all data protection-related problems, but also the use of distributed data that may not be combined centrally for legal or institutional reasons, repeatedly cause problems. In research, too, there are always results that could be passed on by the surveyors to other institutions, whether from research or industry, in order to, for example, be of use in other fields of application. Data markets (see [5] for a formal description) seem to be the appropriate answer to these needs. They allow the distribution of data, whether processed, anonymised or in raw form, as well as the development of a business model based on the transfer of one's own data. Therefore, there are already some initiatives aiming at the establishment of data markets; \"Data Market Austria\" [6] is a prominent example in which many national efforts are bundled. Similar initiatives also exist internationally, such as the IOTA Marketplace [7], the planned Food Data Market [8] or the targeted and GDPR-compliant search for patient data in the H2020 project FeatureCloud [9]. All these initiatives and concepts are strictly centralised and service oriented, i.e., a central operator operates a central market (also with distributed evaluation via block chains or distributed payment via Smart Contracts [10]). This does not necessarily mean that the data traded through these markets are stored on a central location; some concepts let the data reside with the data owners, and solely provide the brokering but, still, the service is centralised to some extent. In addition, services for translation into uniform metadata schemas [11] and the like are offered. However, the use case of these data markets always presupposes that organisations trade with other external organisations (under certain conditions) ready-made data sets and a quasi-free market is created in which services can be offered at a later stage. In [12], the authors also formally described the different roles and trust relationships within such a data market for the first time. The term \"internal data market\" was coined in [13] and provides a first, basic idea on remunerating the provision of data inside a large company or institution. Their definition of an internal data market is quite abstract and consists of three entities: the infrastructure, a buyer and a seller. It must be noted that their concept is not derived from global data markets, or any other form of data brokering with external partners, but from an ERP (Enterprise Resource Planning) perspective, alongside other types of markets for services (e.g., marketing) inside companies [14]. Other authors [15] derive the definition of an internal data market from the data-sharing perspective and make a strong distinction between \"external\" and \"internal\" markets within an organisation, or, more generally, within a narrowly defined and consolidated consortium. Especially in the direction of the economic component of data markets, there are already some works on pricing [16] and fairness [17] amongst others, as well as work on establishing data markets for specific business models [18]. A somewhat dual concept to data markets, providing machine learning as a service, is also proposed in the literature (see e.g., [19]).