Here I keep structured paper reviews, research reflections, and technical deep dives on papers I read.
This section contains detailed analyses of research papers related to my core interests. I focus on understanding how theoretical techniques such as homomorphic encryption, secure multi-party computation, and differential privacy are applied in real systems.
Each deep dive includes a structured breakdown of the paper’s motivation, methodology, key contributions, limitations, and potential research directions.
Published:
This post is based on the paper TFMD: General and Fast Secure Neural Network Inference Framework with Threshold FHE.
이 글은 TFMD: General and Fast Secure Neural Network Inference Framework with Threshold FHE 논문을 기반으로 정리한 내용이다.
Published:
This post is based on the paper FedRecovery: Differentially Private Machine Unlearning for Federated Learning Frameworks.
이 글은 FedRecovery: Differentially Private Machine Unlearning for Federated Learning Frameworks 논문을 기반으로 정리한 내용이다.
Published:
This post is based on the talk Learning and Unlearning Your Data in Federated Settings (PEPR ‘24, USENIX).
이 글은 Learning and Unlearning Your Data in Federated Settings 발표를 기반으로 정리한 내용이다.
Published:
This post is based on the paper MaskCRYPT: Federated Learning With Selective Homomorphic Encryption.
이 글은 MaskCRYPT: Federated Learning With Selective Homomorphic Encryption 논문을 기반으로 정리한 내용이다.
Published:
This post is based on the paper HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption.
이 글은 HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption 논문을 기반으로 정리한 내용이다.