Structured paper reviews and research notes on privacy-preserving and verifiable machine learning. Each entry covers the method, then the limitations and open questions — usually the more interesting part.
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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.
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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.
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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.
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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.
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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.