Privacy Preserving Synthetic Health Data

Andrew Yale (1) , Saloni Dash (2) , Ritik Dutta (3) , Isabelle Guyon (4, 5, 6) , Adrien Pavao (5, 6, 4) , Kristin P Bennett (1)

1 RPI - Rensselaer Polytechnic Institute
2 BITS Pilani - Birla Institute of Technology and Science
3 IIT Gandhinagar - Indian Institute of Technology [Gandhinagar]
4 TAU - TAckling the Underspecified
5 LRI - Laboratoire de Recherche en Informatique
6 UP11 UFR Sciences - Université Paris-Sud - Paris 11 - Faculté des Sciences

We examine the feasibility of using synthetic medical data generated by GANs in the classroom, to teach data science in health infor-matics. We present an end-to-end methodology to retain instructional utility, while preserving privacy to a level, which meets regulatory requirements: (1) a GAN is trained by a certified medical-data security-aware agent, inside a secure environment; (2) the final GAN model is used outside of the secure environment by external users (instructors or researchers) to generate synthetic data. This second step facilitates data handling for external users, by avoiding de-identification, which may require special user training, be costly, and/or cause loss of data fidelity. We benchmark our proposed GAN versus various baseline methods using a novel set of metrics. At equal levels of privacy and utility, GANs provide small footprint models, meeting the desired specifications of our application domain. Data, code, and a challenge that we organized for educational purposes are available.

Domaines: Intelligence artificielle [cs.AI]; Statistiques [math.ST]; Machine Learning [stat.ML]; Médecine humaine et pathologie

Andrew Yale, Saloni Dash, Ritik Dutta, Isabelle Guyon, Adrien Pavao, et al.. Privacy Preserving Synthetic Health Data. ESANN 2019 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2019, Bruges, Belgium. ⟨hal-02160496⟩