# Security and Ownership in User-Defined Data Meshes
## 1 Introduction  
Nowadays, big data can be characterized as the \"new oil\", recognized as a valuable human asset. The effective aggregation and analysis of these data may unearth information that provides insights into numerous facets of everyday activities and offers the ability to anticipate future occurrences. Big data refer to the substantial volumes of digital information consistently produced by machines and the global population from diverse sources such as social media, Internet of Things (IoT) devices, machines and sensors logs, public records and open data, online transactions, websites and applications, research and scientific instruments, etc. [1]. The vast majority of big data originate from heterogeneous data sources, yielding a variety of data types that include structured, unstructured, and semi-structured data. Encompassing a diverse range of content, big data span from textual information to multimedia elements, such as images, videos, and audio [2]. The three primary characteristics (3 Vs) of big data, as presented by [PERSON] in 2001, form and define its fundamental framework [3]. Firstly, _Volume_ represents the broad amount of data generated from data sources, often reaching high levels that challenge typical data processing methods. The second characteristic defining the speed at which data are created, processed, and made available for analysis is denoted by _Velocity_. Fast processing speeds are required to keep up with the increasing rate of data creation due to the emergence of real-time data sources like social media and sensors. Thirdly, the term _Variety_ highlights the variety in types of data, encompassing organized, unstructured, and semi-structured information. By integrating a broad range of textual, visual, and audioinformation, this inclusivity recognizes that big data extend beyond traditional databases. Taken together, these three qualities create the foundation for realizing and capitalizing on the possibilities of big data in a data-driven modern world. In addition, seven more characteristics were added to this list after 2001, thus creating the 10 Vs as terms related to big data. The new properties are _Value, Veracity, Volatility, Validity, Vulnerability, Variability_, and _Visualization_[4] and offer additional descriptive assets of big data. In the pre-big data era, storage designs were mostly based on file systems and conventional relational databases. Relational databases with clear schemas, like MySQL and Oracle, were great at handling structured data. A lot of people used file-based storage systems, such us Network Attached Storage (NAS) and Storage Area Network (SAN), to store documents and other kinds of files. During the same period, the conventional approach to address escalating data requirements involved vertical scaling, which entailed augmenting resources on a single server [5]. In the era of big data, which is characterized by immense data volumes, rapid data transfer rates and the diversity of weakly structured data from numerous heterogeneous sources, as also declared by the 10 Vs characteristics, resulted in a fundamental transformation of storage architectures. NoSQL databases, such as MongoDB and Cassandra, as well as distributed storage systems like Hadoop Distributed File System (HDFS), have now become more popular [6]. The complex interactions amongst data lakes, data meshes, and data Markets in the big data era have created a dynamic ecosystem that transforms how businesses manage and extract value from heterogeneous data sources and big data [7]. Data meshes and data markets are innovative data management frameworks first introduced by [PERSON] in 2019, diverging from the conventional approach of data lake storage architectures. These storage architectures and structures can be deployed using storage and processing technologies, such as Apache Hadoop, Apache Spark, or cloud-based solutions such as Amazon S3, Azure Data Lake Storage, or Google Cloud Storage [8].