# Security and Ownership in User-Defined Data Meshes
## 1 Introduction
While these frameworks are linked with big data processing, the primary unsolved challenging problems revolve around security, encompassing issues related to privacy, regulatory requirements, and access control. Notably, weaknesses in metadata management pose challenges, as data in lakes or meshes can be replaced without proper management of their contents [9]. The primary research contribution of this paper lies in the introduction of an innovative framework that leverages semantic data blueprints (SDB) [10] for the dynamic assembly of Data Meshes and data products responding to user demands on the one hand, and ensuring that stakeholders access specific areas of the data mesh as needed via transfer of ownership on the other. The integration of non-fungible tokens (NFTs) and Blockchain technology collaboratively establishes a novel approach to address data ownership and governance concerns. The core of this framework is a dedicated algorithm that involves the execution of specific steps to facilitate secure and transparent data ownership transfers by combining the ability to mint time-based NFTs with extended functionality. The proposed approach expands earlier research on the subject that proposed SDB, a semantic metadata enrichment technique for data lakes that enables the effective storing and retrieval of data from distributed and heterogeneous data sources, as well as ensuring security in data lakes using Blockchain technology and NFTs [10; 11; 12]. The same concepts are employed in this study, but this time they align with the characteristics of data meshes, ensuring security and ownership through the integration of Blockchain and NFT technology, thereby paving the way for the development of data markets. In this context, a data mesh is thought of as the evolution of a data lake in terms of managing massive amounts of data (big data) expressed in a variety of formats (structured, unstructured, and semi-structured), but most crucially, for making it simple, rapid, and effective to trace. Users and their preferences or needs shape the form and variety of data products synthesized within the Data Meshes. Users initiate a series of functions within the system, including data retrieval from a data lake, data product construction, and ownership transfer. Through this iterative process, the framework generates a diverse array of outputs, encompassing both traditional data products and NFTs, something that underscores the collaborative nature of data management and emphasizes its adaptability to accommodate the evolving needs and preferences of its users. Real-world manufacturing data from Paradisiotis Group (PARG), a significant local industrial player in Cyprus, are used to illustrate the proposed approach. PARG is one of the most significant companies, comprising experts in the field of poultry farming and the production/trading of poultry meat in Cyprus. It provides a large assortment of food products that are delivered to local supermarkets. The operational procedures and production data of the factory are treated as confidential for privacy and security reasons. Consequently, this study uses a masked and de-identified rendition of the data and only presents a portion of the processes, providing limited but specific details. However, the case study reported in the present paper successfully illustrates the fundamental ideas of the proposed framework, confirming its applicability and effectiveness. The remainder of the paper is structured as follows: Section 2 discusses the technical background and related research in the areas of data lakes and data meshes. Section 3 outlines previous research performed on the semantic enrichment technique, which is adopted and extended in this work to address security and ownership aspects. Section 4 presents the extended data meshes framework and discusses its main components. This is followed by demonstrating the applicability and assessing the performance of the proposed framework in Section 5 through a case study conducted using real-world data collected at PARG.