# Secure Internal Data Markets
## 4 A Generalised Model for Secure Internal Data Markets
### Comparison to Other Data Market Approaches  
Much literature has been put forward in the past in order to provide practical approaches for data markets, especially considering market dynamics (e.g., [34]), but also focusing on security and privacy aspects (e.g., [35]). Several important approaches have been put forward in recent years, with a strong focus on using blockchains as a major backbone technology. In [36], the authors propose a novel approach for a blockchain-based market called Omnilvtics for matching research data with models that require training, while focusing on the issues of model privacy, data privacy and byzantine resistance. While the resulting model is highly distributed, and could be used as a local installation, it is developed with a very specific use case in mind, following a no-trust approach between the different partners. Still, it is far more specific when compared to the basic model outlined in our work, which is a positive aspect in terms of direct applicability, but also emphasises the key finding in our work that the current research is focusing on providing one specific model for data markets. As we outlined in Section 3, internal markets might have many different issues at hand, depending on the actual setup in question. OmniLytics answers a rather specific setup, which, from the basic idea, rather hints at a global market solution. This is also quite similar to [17], which is a different blockchain-based approach that focuses on issues of data availability, fairness in the payment process and user privacy, i.e., the data consumer must not be able to determine the real identity of the data provider. Again, this is a rather specific requirement, unsuitable for many applications, especially in internal data markets, where the identity of the data provider is (i) obvious based on the company structure and (ii) even required to be guaranteed, e.g., in order to guarantee the usability ofthe data with respect to technical and legal questions. Again, these requirements might be useful in a global market perspective, but hardly in a local one. In [37], the authors provide a lot of insight in using blockchains for data markets, again focusing on the global case. BlockMarkchain [38] is another example of a very interesting approach for building a data market in a untrusted environment by using blockchain, especially focusing on performance issues. Still, these basic requirements are very untypical for many applications inside companies, as there typically is already a multitude of trusted and semi-trusted \"third\" parties involved, e.g., the IT department, management or the legal department, rendering the whole issue of no trust between participants negligible. Using blockchain (or other digital ledger technologies) especially makes sense in untrusted environments with no trusted third party available, as it can be seen in global data markets. From an internal data market perspective, i.e., from the perspective of a company that sets up an internal data market, this requirement most certainly does not hold for most application aspects (also see [15]). Still, in our basic approach, we also utilise a integrity protection measure, which can be based on a blockchain, especially in the Audit and Control (see Section 4.2). Here, the idea is not to push the whole trading mechanism to the blockchain, but merely to provide defence mechanisms against internal attackers, e.g., database administrators, as these roles are far more powerful in an internal market than an external counterpart, as they act as (semi-) trusted entities. Thus, the internal mechanisms of the market application need to defend the integrity of their data against a powerful internal adversary, in contrast to the approaches outlined above that focus on detecting market partners that try to game the market, either on the data or the payment side.