# Modeling Data Sovereignty in Public Cloud--A Comparison of Existing Solutions
## 6 Conclusions and Future Research Directions  
The abstract data sovereignty model introduced in this paper aids in designing components that support data spaces by following a layered approach, which separates the purposes of different independent software building blocks. The experiments confirmed that the most significant factor affecting the performance of the Dataspace Connector is the geographical location of the virtual server (see Figure 3), rather than the cloud platform hosting it. Additionally, only minimal differences in the performance of GET and POST HTTPS requests were measured. The experiments conducted in this research are limited to one implementation of the IDS connector, two HTTP endpoints, and two cloud service platforms. Future research steps could involve running the Dataspace Connector on edge devices, utilizing public cloud serverless managed services (e.g., GCP Cloud Run or Azure Container apps), and extending the test environment to include Amazon Web Services (AWS). Additional API endpoints can also be included in the test to perform actual data access, allowing the measurement of the time required for policy enforcement. We observed that the main impact on connector performance was related to geographical location, so future research could extend the list of locations. Since various cloud providers and edge devices may have different network connections, it is important to compare different connector hosting options. The comprehensive set of experiments conducted on three major hyperscale cloud service providers, along with edge hosting options and testing several implementations of the connector service, will provide a more thorough understanding of the performance of data sovereignty solutions in the public cloud. It should be noted that conducting experiments in public cloud environments introduces additional complexity, which may impact results--particularly when measuring response times--due to factors such as network variability. To mitigate this risk and ensure reliable results, it is recommended to repeat tests at different times and locations. This approach also highlights a challenge for future experiments, as cloud providers host data centers in diverse geographical locations. However, future experiments can be designed to remotely initiate test scripts across various combinations of target virtual servers and test-triggering locations, enabling more robust and representative data collection. The practical implications of this study for organizations influencing decision-making processes related to implementing data sovereignty solutions in public cloud environments are twofold. First, production-ready, off-the-shelf solutions are available, such as those developed by IDSA, FIWARE, and GAIA-X. Second, the abstract data sovereignty model introduced in this paper provides a framework for constructing customized solutions, allowing organizations to build systems from distinct components assembled within defined functional layers. As discussed in the literature review, different data sovereignty requirements necessitate distinct sovereignty solutions, which may range from privacy-shielding services on public cloud platforms to fully isolated private cloud solutions. Generally, practitioners and policymakers involved in data governance decisions should begin with an in-depth analysis of specific requirements. Based on this analysis, they can then assess whether any of the existing solutions presented in this paper meet all criteria, or if a completely new system must be designed using the abstract data sovereignty model outlined in Section 4. Conceptualization, [PERSON] ([PERSON]) and G.P. ; methodology, G.P. ; software, [PERSON] ([PERSON]); validation, G.P. and S.G. ([PERSON]); formal analysis, G.P.