YoVDO

Fast Homomorphic Evaluation of Deep Discretized Neural Networks

Offered By: TheIACR via YouTube

Tags

Homomorphic Encryption Courses Deep Learning Courses Neural Networks Courses Cryptography Courses

Course Description

Overview

Explore a conference talk on fast homomorphic evaluation of deep discretized neural networks, presented at Crypto 2018. Delve into the research by Florian Bourse, Michele Minelli, Matthias Minihold, and Pascal Paillier, which addresses challenges in neural network evaluation using homomorphic encryption. Examine the problem statement, neural network applications in digit recognition, and the current state of the art. Learn about the Multisim approach, discretized network evaluation, and activation issues. Understand the concept of dynamic message space and the overall process. Review experimental results, the proposed framework, and discuss open problems in this field of cryptography and machine learning.

Syllabus

Introduction
Problem Statement
Neural Networks
Digit Recognition
State of the Art
Multisim
Discretized Network
Evaluation
Activation
Issues
Dynamic Message Space
Process Overview
Experimental Results
Framework
Open Problems


Taught by

TheIACR

Related Courses

Neural Networks for Machine Learning
University of Toronto via Coursera
機器學習技法 (Machine Learning Techniques)
National Taiwan University via Coursera
Machine Learning Capstone: An Intelligent Application with Deep Learning
University of Washington via Coursera
Прикладные задачи анализа данных
Moscow Institute of Physics and Technology via Coursera
Leading Ambitious Teaching and Learning
Microsoft via edX