Name of participant: Yunxuan Li
Project’s name: Adaptive Privacy Protection for Modern Data Architectures (PanDA)
Project description:
In today’s data-centric world, data has become a vital resource, empowering organizations to gain insights and develop new business models through advanced processing and analysis. However, the handling of personal and sensitive data raises significant privacy concerns, since inadequate data protection practices can lead to violations of data protection regulations and a loss of customer trust.
Privacy is often perceived as an individual’s control over their personal data. However, established privacy theories, such as Altman’s privacy regulation theory and Nissenbaum’s contextual integrity theory, emphasize that privacy is a dynamic and adaptive process where individual privacy expectations and privacy regulation behaviors are subject to changing contexts, environments or personal preferences. Therefore, Schaub et al. highlight that privacy protection should be considered as a continuous and adaptive process rather than a static approach. This necessitates privacy protection solutions that can dynamically adapt to varying situations.
To cope with the increasing volume and variety of data, modern data architectures such as data lakes, lakehouses, and data meshes have been introduced to provide scalable, flexible, and agile data management. Despite these advancements, privacy considerations are often insufficiently integrated into data architecture designs. Existing approaches either remain at a conceptual level or rely on static, pre-configured privacy protection mechanisms that apply uniform data privacy measures across all scenarios. Such approaches lack the flexibility to adapt to situational changes and fail to recognize privacy as a dynamic process. Therefore, the objective of this project is to investigate how privacy protection can be integrated into modern data architectures by design, taking into account their ever-changing environments, such as evolving data sources or use cases, while ensuring high data utility and agile data management.
Software Campus Partner: Uni Stuttgart and DATEV eG
Implementation period: 01.03.2025 – 28.02.2027
