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20212026
most citedPrivacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network

4 citations · 8 across the 6 of their papers we have counts for

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cs.CR2026

HE-Guardrail: A Homomorphic Guardrail Against Jailbreak Attacks for Encrypted Large Language Model Inference

Byeongseo Min, Yongwoo Lee, Young-Sik Kim +1

Homomorphic encryption (HE) has emerged as a promising approach to privacy-preserving machine learning (PPML), enabling computation directly over encrypted data. In HE-based PPML,…

cs.CR2026

CGF-Softmax: A Cumulant-Based Softmax Reformulation for Efficient Inference under Homomorphic Encryption

Hanjun Park, Byeongseo Min, Jiheon Woo +5

Homomorphic encryption (HE) is a prominent framework for privacy-preserving machine learning, enabling inference directly on encrypted data. However, evaluating softmax, a core com…

cs.CR2025

Mutual Information Minimization for Side-Channel Attack Resistance via Optimal Noise Injection

Jiheon Woo, Donggyun Ryu, Daewon Seo +4

Side-channel attacks (SCAs) pose a serious threat to system security by extracting secret keys through physical leakages such as power consumption, timing variations, and electroma…

cs.CR2023★ 2 cited

Optimized Layerwise Approximation for Efficient Private Inference on Fully Homomorphic Encryption

Junghyun Lee, Eunsang Lee, Young-Sik Kim +4

Recent studies have explored the deployment of privacy-preserving deep neural networks utilizing homomorphic encryption (HE), especially for private inference (PI). Many works have…

cs.CR2022★ 1 cited

Green Bitcoin: Global Sound Money

Heung-No Lee, Young-Sik Kim, Dilbag Singh +1

Modern societies have adopted government-issued fiat currencies many of which exist today mainly in the form of digits in credit and bank accounts. Fiat currencies are controlled b…