180 citations · 382 across the 6 of their papers we have counts for
6 papers
Toward Practical Privacy-Preserving Convolutional Neural Networks Exploiting Fully Homomorphic Encryption
Jaiyoung Park, Donghwan Kim, Jongmin Kim +4
Incorporating fully homomorphic encryption (FHE) into the inference process of a convolutional neural network (CNN) draws enormous attention as a viable approach for achieving priv…
AESPA: Accuracy Preserving Low-degree Polynomial Activation for Fast Private Inference
Jaiyoung Park, Michael Jaemin Kim, Wonkyung Jung +1
Hybrid private inference (PI) protocol, which synergistically utilizes both multi-party computation (MPC) and homomorphic encryption, is one of the most prominent techniques for PI…
BTS: An Accelerator for Bootstrappable Fully Homomorphic Encryption
Sangpyo Kim, Jongmin Kim, Michael Jaemin Kim +4
Homomorphic encryption (HE) enables the secure offloading of computations to the cloud by providing computation on encrypted data (ciphertexts). HE is based on noisy encryption sch…
Accelerating Number Theoretic Transformations for Bootstrappable Homomorphic Encryption on GPUs
Sangpyo Kim, Wonkyung Jung, Jaiyoung Park +1
Homomorphic encryption (HE) draws huge attention as it provides a way of privacy-preserving computations on encrypted messages. Number Theoretic Transform (NTT), a specialized form…
HEAAN Demystified: Accelerating Fully Homomorphic Encryption Through Architecture-centric Analysis and Optimization
Wonkyung Jung, Eojin Lee, Sangpyo Kim +5
Homomorphic Encryption (HE) draws a significant attention as a privacy-preserving way for cloud computing because it allows computation on encrypted messages called ciphertexts. Am…
Restructuring Batch Normalization to Accelerate CNN Training
Wonkyung Jung, Daejin Jung, and Byeongho Kim +3
Batch Normalization (BN) has become a core design block of modern Convolutional Neural Networks (CNNs). A typical modern CNN has a large number of BN layers in its lean and deep ar…