Bioinformatics Seminar - Hoon Cho: Computational tools for understanding and addressing privacy challenges in genomics, 32-G575

The sensitivity of human genomic data poses significant challenges for data sharing and collaboration in biomedicine. Balancing privacy protection and scientific progress is crucial. In this talk, I will discuss our recent efforts to develop effective algorithms that deepen our understanding of genomic privacy risks and facilitate secure data sharing. First, I will describe our novel probabilistic approach to modeling associations between gene expression levels and genotypes at the sequence level. This approach reveals a greater extent of data linkage risks than previously recognized. Next, I will describe our data-oblivious genomic analysis algorithms, designed for deployment in trusted execution environments (TEEs). These tools can power secure analytic services, providing confidential processing of a user’s genome. Lastly, I will introduce our suite of secure and federated (SF) algorithms for essential tasks including genome-wide association studies (GWAS), incorporating modern techniques from applied cryptography, distributed algorithms, and statistical genetics. These tools enable collaborative studies across large-scale genomic data silos, encompassing hundreds of thousands of genomes, while ensuring the confidentiality of each dataset. Our work lays the foundation for broader collaboration in biomedicine, advancing progress while respecting individual privacy.

Zoom link: https://mit.zoom.us/j/93513735220

Room 32-G575