HealthCare: Attempting to Clear the Analytics Privacy Hurdle

P4 is a future vision for medicine: It is Predictive, Preventitive, Personalized, and Participatory. A principle goal of P4 is being able to assess the wellness of an individual and to be able to present wellness data in a totally comprehensible way. P4 will enable the development of more effective disease treatments and reduce the incidence of disease.

Leroy Hood, biologist and medical researcher and inventor, said that “almost everyone practices 20th Century medicine right now. And it’s going to be a fundamental paradigm change to practice 21st Century medicine.” Hood is an advocate of “scientific wellness” which emphasizes high-thoughput processing of biological data and the application of analysis technologies like analytics and AI.

“There are two central thrusts in healthcare,” said Hood. “One is wellness and the other is disease.” While the 20th Century healthcare focused almost exclusively on the latter, recent years have – slowly – seen a shift imperative to prioritize health and wellness.

P4 Medicine uses information technologies like big data and artificial intelligence to better understand disease and the interactions between biology and environment. But privacy issues related to the secure collection, aggregation, and sharing of health data have been a road block for using large-scale analytics.

Recent research being done by a collaboration of organizations including MIT CSAIL, Lausanne University Hospital, EPFL Laboratory, and Harvard have devised a secure encryption method called FAMHE for sharing health data without privacy and sensitivity issues.

Jean-Pierre Hubaux, EPFL Professor, said that “until now, no one has been able to reproduce studies that show that federated analytics works at scale. Our results are accurate and are obtained with a reasonable computation time. FAMHE uses multiparty homomorphic encryption, which is the ability to make computations on the data in its encrypted form across different sources without centralizing the data and without any party seeing the other parties’ data.”

Bonnie Berger, professor at MIT, said that “this is a game-changer towards personalized medicine, because, as long as this kind of solution does not exist, the alternative is to set up bilateral data transfer and use agreements, but these are ad hoc and they take months of discussion to make sure the data is going to be properly protected when this happens. FAHME provides a solution that makes it possible once and for all to agree on the toolbox to be used and then deploy it.”