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Imagine this: The key to better cancer treatments is within reach, based on patterns from data that is scattered across various locations all over the world. This data could be digitalised, labelled, collected, stored and interpreted. However, this data belongs to a countless number of individuals – and their right to data privacy weighs just as much as the dream of curing a lethal disease.
That is exactly the dilemma we face. Potentially life-saving and cost-cutting solutions, such as significant improvement of early diagnosis, real time remote diagnosis or identification of promising treatment options are impossible because data privacy needs to be ensured. Standard ways of anonymisation cannot reliably prevent re-identification of individual patients. But is there any way in which an algorithm trained on encrypted data could produce informative, actionable results?
In a collaboration between computer scientists, mathematicians and computational medical scientists at the City University of New York and University of Michigan (Ann Arbor), we applied full homomorphic encryption (FHE) to classify breast cancer data as benign or malignant – completely preserving data-privacy. The lesson is clear: FHE could be the foundation for bringing healthcare into the digital era.
The event took place on July 13th, 2018.
Find Delaram’s slides on SlideShare.
