Special Edition Blog Series on PhD Abstracts (Part I)
In this special edition series of blog posts, we are excited to present the PhD abstracts of our 15 Early Stage Researchers (ESRs). Each ESR has not only contributed to the interdisciplinary research within the LeADS project and its four Crossroads but has also pursued their own individual research within the scope of their PhD thesis.
While the topics and titles of their PhD theses may not align exactly with the specific LeADS research areas assigned to them, the influence of their work within the project has undoubtedly shaped and enriched their doctoral research. This diversity of topics reflects the depth and breadth of inquiry fostered within the LeADS project. We invite you to explore a variety of research topics and witness the valuable insights developed throughout the research journeys of our ESRs.
Qifan Yang: Reciprocal interplay between personal data protection under the GDPR and market competition in the data-driven society.
With the rapid development of the data economy, data has gradually become the key input and critical production factor and extracting value from big data has also been a significant source of power for internet market players. The review of the process of data generation reveals that most valuable data are produced by users.The frequent and massive collection and processing of data in the digital age have raised concerns about data privacy leaks and misuse. The EU General Data Protection Regulation covers personal data protection and cross-border transfers in the hope to tackle the protection of data subjects and its complex interrelation with economic and political implications via a comprehensive legal regime.
Against this backdrop scenario, as a rule of market governance, personal data protection seeks the balance between economic interests and individual rights taking into account the differences in their sensitivity. Although we cannot measure every influencing factor and turn them into conditions for a desired model, this research project will analyse the debate and impacts of the data protection regulation on competition dynamics in the EU and other countries, especially the impacts of personal data protection on the consolidation of market dominance. Due to the reciprocal interplay between competition law and personal data protection, personal data protection is also affected by competition law in a constant loop reaching different equilibria. Therefore, another important research objective is to sketch the mechanisms through which competition law can have an impact on data privacy in the legal and economic context. Methodologically, this research will be leveraging relevant legal, economic, technical and combining both a theoretical methodology with empirical analysis.
Louis Sahi: Distributed reliability and blockchain like technologies.
Data processing and AI-based techniques are now widely used in multiple sectors, including business, sociology, healthcare, mobility, research, etc. Moreover, companies and public organizations have produced and/or collected various types of data which today are stored in data silos that need to be integrated to build a data economy that drives innovation. Such data spaces should involve different stakeholders in collaborative data processing including distributed data life cycle as well as decentralized data governance. Naturally, when several systems are interconnected to carry out each step of the data life cycle, this data life cycle can be defined as distributed. When multiple entities manage data governance, this type of data governance is called decentralized data governance. Collaborative data processing raises several issues and challenges, especially, ensuring the reliability of distributed systems, trust in the decentralized governance of data processing, and compliance with legal requirements concerning data processing. Data quality plays a central role in these challenges to create a data economy. Data quality evaluation is a potential indicator to enhance the reliability, trust, and legal compliance of shared data across collaborative data processing. The main contribution of my research will respond to questions such as: are data governance stakeholders able to make the right decisions to maintain data quality? What are the data quality criteria that can be used to assess trust in all data governance stakeholders based on their actions and decisions? What are the data quality criteria pertinent to data governance? Then, how to assess the reliability of all components in distributed systems, i.e. the ability of each component to perform correctly and not degrade the quality of the data? How to create data quality contracts at each step of the data life cycle based on appropriate data quality criteria? Finally, how do we respond to the fact that there is no existing work that categorizes data quality criteria according to different EU regulations, such as the GDPR, the Data Act, or the Data Governance Act?
