Speaker: Dr.Seungwan Hong

Title: Homomorphic Encryption for Secure Data Analysis                                                                           

Abstract:  Homomorphic encryption (HE) is a cryptographic technique that enables computation directly on encrypted data, providing a foundation for secure and privacy-preserving data analysis. In this talk, I will introduce the principles of HE and discuss its potential to transform how sensitive data can be utilized without compromising confidentiality. I will present my research on advancing algorithms and applications of HE across diverse domains, with a particular focus on the intersection of cryptography, machine learning, and bioinformatics.

Bio: Seungwan Hong is a Postdoc jointly affiliated with the New York Genome Center and Columbia University. He received his Ph.D. in Mathematics from Seoul National University, where he specialized in cryptography. His research focuses on homomorphic encryption and its applications to privacy-preserving computation, including algorithm design, privacy-preserving machine learning, and secure analysis of genomic data.