21 citations · 33 across the 4 of their papers we have counts for
4 papers
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…
Medha: Microcoded Hardware Accelerator for computing on Encrypted Data
Ahmet Can Mert, Aikata, Sunmin Kwon +4
Homomorphic encryption (HE) enables computation on encrypted data, and hence it has a great potential in privacy-preserving outsourcing of computations to the cloud. Hardware accel…
Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network
Joon-Woo Lee, HyungChul Kang, Yongwoo Lee +8
Fully homomorphic encryption (FHE) is one of the prospective tools for privacypreserving machine learning (PPML), and several PPML models have been proposed based on various FHE sc…
Iterative DNA Coding Scheme With GC Balance and Run-Length Constraints Using a Greedy Algorithm
Seong-Joon Park, Yongwoo Lee, Jong-Seon No
In this paper, we propose a novel iterative encoding algorithm for DNA storage to satisfy both the GC balance and run-length constraints using a greedy algorithm. DNA strands with…