# Security and Ownership in User-Defined Data Meshes
## 3 Related Research  
The combination of DLs, DMs, and Blockchain-based technologies--specifically, NFTs--in the field of modern data management creates a dynamic synergy that is changing how businesses handle data ownership, accessibility, and storage. DLs function as large storage spaces for heterogeneous data, promoting a single repository that can handle a variety of data types. In addition, the DM paradigm supports distributed data processing and domain-oriented ownership using decentralized data architectures. A new dimension is brought to data ownership and authenticity by the integration of NFTs on Blockchain platforms, which offer a safe and verifiable framework for identifying the provenance and ownership of individual pieces of data. The integration of Blockchain, DLs, and DMs improves the scalability and flexibility of data ecosystems and lays the groundwork for more open, safe, and cooperative data management procedures, as related work reveals. A dedicated DL architecture was used in [13] to investigate how Blockchain technology might be integrated to improve the scheme's metadata management. It specifically presented the use of NFTs that are stored on the Blockchain to represent metadata for every data source. The intention was to use Blockchain technology to improve the DL's semantics metadata, which could lead to better data management, organization, and retrieval. Furthermore, ref. [11] addressed the challenges associated with the smart processing of big data in the context of DLs, as well as ownership and security, using Blockchain and NFT technologies. It emphasized the need for a disciplined approach to manage diverse data sources within DLs for predictive and prescriptive analytics. That paper introduced a novel standardization framework that integrates the 5 Vs of big data characteristics and blueprint ontologies. The framework utilized a pond architecture to organize DLs and incorporated a metadata semantic enrichment mechanism for efficient storage and retrieval. Notably, the mechanism supported visual querying and enhanced security through Blockchain and NFTs. The authors also provided a comparative analysis with other metadata systems, demonstrating promising results based on a set of functional properties. An enhanced DL metadata framework called DLMetaChain was introduced in [12], which can manage data from diverse sources like IoT data using Blockchain. The paper discussed the changing IoT ecosystem, where a variety of sources produce large amounts of data that are then converted into useful information. Metadata management becomes difficult when storing such data, including IoT data, in repositories such as DLs, especially when it comes to security and access control. The principal aim was to design an architecture that utilizes Blockchain technology to guarantee the data integrity of the DL by impeding any unsanctioned changes or additions. A visionary approach to establish a distributed federated medical DL and ecosystem was proposed in [22], involving hospitals and personal health data from wearable medical devices. It emphasized the creation of a Blockchain-based platform with commercial incentives, addressing data ownership, patient privacy, and controlled access. The platform facilitated owner-centric medical data exchange, securely aggregated data from various hospitals, and unlocked academic and business value by representing medical data as NFTs. The primary goal was to improve healthcare research while fostering a sustainable medical data ecosystem. Our framework builds upon existing research by integrating privacy engineering principles into decentralized data architectures [23], particularly focusing on DM. Unlike previous work, we propose a comprehensive algorithmic framework utilizing data blueprints to enable dynamic creation and governance of DMs and data products. Leveraging Blockchain and NFTs, our approach addresses privacy concerns, ownership, governance, and transparency.