180 citations · 382 across the 6 of their papers we have counts for
4 papers · 1 filter
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…