Researchers around the globe are gathering biomedical information at a massive scale. However, privacy and intellectual property concerns hinder open sharing of these data, presenting a key barrier to collaborative science. In this talk, I will describe how modern cryptographic tools present a path toward broader data sharing and collaboration in biomedicine as demonstrated by my recent work on genome-wide association studies (GWAS) and pharmacological machine learning. For each domain, I will introduce our efficient privacy-preserving analysis protocol that achieves state-of-the-art accuracy while ensuring the input data remain private throughout the protocol. Our protocols newly achieve scalability to a million genomes or drug compounds by drawing on a set of techniques aimed at reducing redundancy in computation. Key components of our pipelines, including secure principal component analysis (PCA) and secure neural networks, are broadly applicable to other data science domains. These results lay a foundation for more effective and cooperative biomedical research where individuals and institutes across nations pool their data together to enable novel life-saving discoveries.
Hyunghoon (Hoon) Cho is finishing his Ph.D. in Electrical Engineering and Computer Science at MIT. Previously, he received his M.S. and B.S. with Honors in Computer Science from Stanford University. His research focuses on overcoming key computational challenges in analyzing massive biomedical data, creating modern tools from mathematics, cryptography and machine learning. He is especially interested in solving problems in the areas of biomedical data privacy, single-cell genomics, and network biology. His presentation on "Secure Genome Crowdsourcing" received the Best Oral Presentation Award at ISMB-TransMed 2018, and his work on "Private and Practical Pharmacological Collaboration" was recently published and featured in Science. During his doctoral studies, he also spent time at Microsoft Research.