AB-DOM: An Algorithmic Framework for Supporting Privacy-Preserving Big Data Publishing in Big Data Lakes
Alfredo Cuzzocrea, Selim Soufargi · IEEE Transactions on Big Data · 2025
With the emergence of new technologies that extend the capabilities of actual data collection methods, healthcare data are more and more amassed in the purpose of being later analyzed to serve the ultimate, well-known, goal of 4P medicine (Predictive, Preventive, Personalized, Participative). Given the sensitive nature of healthcare data, and in a matter of compliance with data protection and privacy regulations, there is a need to make data publishing more secure. This is one of the main goals of theEU H2020 QUALITOP research project, with particular regards to the issue of defining a big health data smart digital platform and the shared data lake. In this context, we design, implement and experimentally assess an innovative algorithmic framework calledAdvanced Privacy-PreservingBig Data Publishing in HierarchicalDOMains(AB-DOM). AB-DOM is based on state-of-the-art anonymization techniques mixed with agraph coloring algorithmand an integrateddata sampling methodto guarantee that sensitive data are highly secured.