OMSCS alumnus @ Georgia Tech. Motivated by my experience working with sensitive data, I aim to design systems that enforce privacy by design while remaining scalable and deployable. My long-term goal is to make strong cryptographic privacy guarantees usable and accessible in practice.
Here I keep structured notes on my explorations, shaped by both professional experience and ongoing exploration.
Core Focus: Privacy-Preserving Machine Learning and Cryptography
I am interested in developing secure and practical methods that protect sensitive data in machine learning systems. My work focuses on bridging the gap between strong privacy guarantees and real-world usability.
My motivation comes from working with real-world data in domains such as healthcare and finance, where privacy and usability often conflict. This has led me to explore how systems can be both secure and deployable.
This post is based on The Beginner’s Textbook for Fully Homomorphic Encryption by Ronny Ko. In this post, I provide a summary and review of “II. Post-quantum Cryptography: B-4. GLWE Cryptosystem.”
This post is based on The Beginner’s Textbook for Fully Homomorphic Encryption by Ronny Ko. In this post, I provide a summary and review of “II. Post-quantum Cryptography: B-3. RLWE Cryptosystem.”
This post is based on The Beginner’s Textbook for Fully Homomorphic Encryption by Ronny Ko. In this post, I provide a summary and review of “II. Post-quantum Cryptography: B-2. LWE Cryptosystem.”
This post is based on The Beginner’s Textbook for Fully Homomorphic Encryption by Ronny Ko. In this post, I provide a summary and review of “II. Post-quantum Cryptography: B-1. Lattice-based Cryptography.”