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 informal notes and concept breakdowns as I learn — definitions, intuitions, and connections I find useful to revisit.
Core Focus: Cryptography and Privacy-Preserving Machine Learning
From cryptographic primitives to privacy-preserving techniques in machine learning, these notes reflect my ongoing effort to build intuition beyond what papers formally explain.
Rather than full paper reviews, each entry captures a concept, a question, or a connection I encountered while reading — written to help myself think more clearly.
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.”