Toward Practical Privacy-Preserving Convolutional Neural Networks
Exploiting Fully Homomorphic Encryption

Toward Practical Privacy-Preserving Convolutional Neural Networks Exploiting Fully Homomorphic Encryption

25 October 2023

Jongmin Kim

Jung Ho Ahn

Papers citing "Toward Practical Privacy-Preserving Convolutional Neural Networks Exploiting Fully Homomorphic Encryption"

9 / 9 papers shown

Title
ARK: Fully Homomorphic Encryption Accelerator with Runtime Data Generation and Inter-Operation Key Reuse Jongmin Kim Gwangho Lee Sangpyo Kim G. Sohn John Kim Minsoo Rhu Jung Ho Ahn 52 100 0 02 May 2022
AESPA: Accuracy Preserving Low-degree Polynomial Activation for Fast Private Inference J. Park M. Kim Wonkyung Jung Jung Ho Ahn LLMSV 52 27 0 18 Jan 2022
BTS: An Accelerator for Bootstrappable Fully Homomorphic Encryption Sangpyo Kim Jongmin Kim M. Kim Wonkyung Jung Minsoo Rhu John Kim Jung Ho Ahn 55 146 0 31 Dec 2021
F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption (Extended Version) Axel S. Feldmann Nikola Samardzic A. Krastev S. Devadas R. Dreslinski Karim M. El Defrawy Nicholas Genise Chris Peikert Daniel Sánchez 105 259 0 11 Sep 2021
Precise Approximation of Convolutional Neural Networks for Homomorphically Encrypted Data Junghyun Lee Eunsang Lee Joon-Woo Lee Yongjune Kim Young-Sik Kim Jong-Seon No 87 58 0 23 May 2021
Accelerating Number Theoretic Transformations for Bootstrappable Homomorphic Encryption on GPUs Sangpyo Kim Wonkyung Jung J. Park Jung Ho Ahn 44 61 0 03 Dec 2020
HeLayers: A Tile Tensors Framework for Large Neural Networks on Encrypted Data E. Aharoni Allon Adir Moran Baruch Nir Drucker Gilad Ezov ... Ramy Masalha Guy Moshkowich Dov Murik Hayim Shaul Omri Soceanu FedML 92 57 0 03 Nov 2020
CrypTFlow2: Practical 2-Party Secure Inference Deevashwer Rathee Mayank Rathee Nishant Kumar Nishanth Chandran Divya Gupta Aseem Rastogi Rahul Sharma 120 314 0 13 Oct 2020
Low Latency Privacy Preserving Inference Alon Brutzkus Oren Elisha Ran Gilad-Bachrach FedML 129 232 0 27 Dec 2018