Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications

10 January 2018

Papers citing "Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications"

8 / 8 papers shown

Title
Flash: A Hybrid Private Inference Protocol for Deep CNNs with High Accuracy and Low Latency on CPU H. Roh Jinsu Yeo Yeongil Ko Gu-Yeon Wei David Brooks Woo-Seok Choi 118 2 0 20 Jan 2025
VeriSplit: Secure and Practical Offloading of Machine Learning Inferences across IoT Devices Han Zhang Zifan Wang Mihir Dhamankar Matt Fredrikson Yuvraj Agarwal 75 2 0 02 Jun 2024
State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey Chaoyu Zhang Shaoyu Li AILaw 84 4 0 25 Feb 2024
Cryptanalytic Extraction of Neural Network Models Nicholas Carlini Matthew Jagielski Ilya Mironov FedML MLAU MIACV AAML 98 135 0 10 Mar 2020
CryptoDL: Deep Neural Networks over Encrypted Data Ehsan Hesamifard Hassan Takabi Mehdi Ghasemi 50 377 0 14 Nov 2017
DeepSecure: Scalable Provably-Secure Deep Learning B. Rouhani M. Riazi F. Koushanfar FedML 37 409 0 24 May 2017
TensorFlow: A system for large-scale machine learning Martín Abadi P. Barham Jianmin Chen Zhiwen Chen Andy Davis ... Vijay Vasudevan Pete Warden Martin Wicke Yuan Yu Xiaoqiang Zhang GNN AI4CE 338 18,300 0 27 May 2016
The Limitations of Deep Learning in Adversarial Settings Nicolas Papernot Patrick McDaniel S. Jha Matt Fredrikson Z. Berkay Celik A. Swami AAML 66 3,947 0 24 Nov 2015