Smart health: ensuring data privacy by providing comprehensive anonymization processes
Thanks to the constant development of communication technology, such as portable mobile devices and wearables, the concept of “smart health” - and with it the continuous improvement of personal health - has become an essential element of modern life for many people. Through the constant collection of data, smart health can promote a healthier lifestyle and also be used to identify possible issues. For example, this technology might lead to a patient starting treatment early or at least in good time.
However, as health data becomes more detailed and accessible to multiple parties, it also becomes vulnerable to attacks on individual patients’ privacy. In order to account for the particular privacy concerns relating to personal medical data, the objective of this project is to develop highly specialized anonymization solutions based on modern data analysis.
The main challenge of this project will be to combine multiple, previously isolated methods into a single integrated open-source demonstrator, and to show that the anonymization capabilities (privacy metrics) of the individual methods can be maintained while also deriving a holistic, combined concept of privacy or anonymization. The project team will focus in particular on identifying and optimizing anonymization combinations, embedding these methods in existing open-source systems, and using machine learning to assess their vulnerability.