Patient Privacy Preservation through Federation or Encryption? A Comparative review and prototypes
The recent advances in machine learning based on deep neural networks, coupled with the availability of phenomenal storage capacity, are transforming the industrial landscape. However, these novel machine learning approaches are known to be data hungry, as they need to tune a huge number of parameters in order to perform well. As more and more AI based applications are being deployed to learn from personal data, privacy concerns are rising, and more specifically on sensible domains like medicine, finance or mobile related data. With the ubiquitous availability of cloud-based solutions at a very low price, privacy has now become even more sensitive. Moreover, privacy concerns seem to be two sided, as service providers would like to keep their models and learned weights private.
This research will focus on studying available solutions for privacy preservation in the context of medical data, and more specifically on volumes obtained from CT scans.