idalab seminar #12: The data-privacy dilemma: How full homomorphic encryption could bring healthcare into the digital era

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.

Friday, July 13th, 5 pm | doors open at 4.30 pm | Potsdamer Straße 68, 10785 Berlin

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About idalab seminars: idalab seminars are open to all interested parties. Once a month, we invite scholars, data scientists, business experts and big data thought leaders to discuss their work, gain new perspectives and generate fresh insights.

After the talk, we invite you to stay for drinks. We’re looking forward to seeing you there!

Prof. Dr. Delaram Kahrobaei

Prof. Dr. Delaram Kahrobaei is an American-Iranian computer scientist, currently a full professor at the City University of New York, in the PhD Program in Computer science, MS Program in Data Science, MS Program in Data Analysis and Visualization at CUNY Graduate Center, at NYCCT Mathematics Department, as well as adjunct professor at the New York University in MS Program in Cybersecurity.

Her recent interests are data mining over encrypted data (Medical and Genomic), as well as post-quantum cryptography. Her research has been supported by grants from ONR, NSF, NSA, AAAS, NASA, IHP, AWM, SNF, LMS, EMS, RF-CUNY among others.

She had several doctoral Students and Post-doctoral fellows who under her supervision have finished their doctorate and obtained prestigious employment within industry and academia. Currently she is the PhD supervisor to two PhD students in computer science, whom will graduate in 2018.

Her research has attracted strong international reputation, in such she has given numerous (200) invited lectures around the world, including: France, Argentina, Israel, Switzerland, Germany, Spain, Wales, England, Scotland, Ireland, Austria, Italy, Canada, USA, Iran, Russia, Netherlands, Sweden, Finland, Basque Country, Australia. She is the general chair of a trimester at Institut Henri Poincare in Paris on “Post-Quantum Algebraic Cryptography” in 2021.

Contact the author
Hannah Martin
+49 (30) 814 513-24