YoVDO

Practical Cryptographic Solutions for Secure Inference and Private Benchmarking

Offered By: TheIACR via YouTube

Tags

Privacy-Preserving Machine Learning Courses Machine Learning Courses Cryptography Courses Federated Learning Courses Data Privacy Courses Differential Privacy Courses Secure Multi-Party Computation Courses Homomorphic Encryption Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore practical cryptographic solutions for secure inference and private benchmarking in this invited talk from the Privacy-Preserving Machine Learning Workshop (PPML) 2024. Delivered by Nishanth Chandran and chaired by Daniel Escudero, the 53-minute presentation delves into cutting-edge techniques for enhancing privacy and security in machine learning applications. Gain insights into the latest advancements in cryptographic methods that protect sensitive data during inference processes and enable private benchmarking of machine learning models. As part of the PPML 2024 event affiliated with Crypto 2024, this talk offers valuable knowledge for researchers, practitioners, and enthusiasts in the fields of cryptography and privacy-preserving machine learning.

Syllabus

PPML 2024 invited talk III by Nishanth Chandran


Taught by

TheIACR

Related Courses

Introduction to Data Analytics for Business
University of Colorado Boulder via Coursera
Digital and the Everyday: from codes to cloud
NPTEL via Swayam
Systems and Application Security
(ISC)² via Coursera
Protecting Health Data in the Modern Age: Getting to Grips with the GDPR
University of Groningen via FutureLearn
Teaching Impacts of Technology: Data Collection, Use, and Privacy
University of California, San Diego via Coursera