While data sharing and collaboration enable many new opportunities for BI, the flip side of the coin is privacy: does the sharing and collaboration (perhaps non-intentionally) expose sensitive information? The aim of this topic is to develop a set of concepts and primitives that add built-in support for privacy into multidimensional BI/OLAP systems. The goal is to protect the (most often cloud-based) collaborative data warehouse by an all-encompassing "privacy shield" that providing integrated privacy management. All queries to the DW must pass through, and be approved by, the shield, thus ensuring that privacy is not violated. A central novel aspect is the invention of a built-in mechanism for balancing data sharing and privacy: some data will be common, some shared with a specific group, and some private to the user. The goal is to develop theoretically sound concepts, effective algorithms, and efficient implementations to fully demonstrate the solution.

The enabling of advanced privacy-preserving data sharing and analysis will open up a range of new applications and opportunities. Diverse parties (private, companies, public) can safely share data with each other and use the integrated view of data to discover new knowledge and thus new business opportunities. This will enable new knowledge-based services and products offered by the industry partners, and open new markets. The developed solution will have applications in many domains, e.g., mobile services, home automation, healthcare, public sector, and energy/smart grid.

Main Advisor at Aalborg University (AAU)
Co-advisor at Université Libre de Bruxelles (ULB)