Seungwan Hong

I am a Postdoctoral Associate at Yale University, working in the Hoon Cho lab. My research focuses on secure genomic analysis, particularly applications of Homomorphic Encryption.

My primary research focuses on practical applications of cryptosystems, especially in homomorphic encryption, privacy-preserving machine learning, and genomic data privacy. I work extensively with Fully Homomorphic Encryption (FHE), optimizing FHE schemes algorithmically and developing algorithms to enable non-arithmetic operations within FHE. In privacy-preserving machine learning, I adapt machine learning algorithms to FHE-compatible formats, utilizing polynomial approximation techniques to achieve secure and efficient computation. My research in genomic data privacy emphasizes designing FHE-based frameworks that enable secure transformations of conventional genomic applications, protecting sensitive genomic information. I am also dedicated to advancing functional encryption by creating schemes that offer enhanced efficiency and functionality.