Secure and Private AI

Online Course

Udacity

What is the course about?
Prerequisites & Facts
Cost, Certificate & Degree
Reviews & Experiences

What is the course about?

Secure and Private AI

The course Secure and Private AI is an online class provided by Udacity. The skill level of the course is Advanced. It may be possible to receive a verified certification or use the course to prepare for a degree.

What’s the earliest we can predict cancer survival rates, and what schools do the best job of educating children? You can only answer these questions with very rare access to private and personal data, but access to this personal data requires that you master methods for the principled protection of user privacy. While not all privacy use cases have been solved, the last few years have seen great strides in privacy-preserving technologies.

Course description

Secure and Private AI
2 months

Learn how to extend PyTorch with the tools necessary to train AI models that preserve user privacy.
Build Deep Learning Models Today
This free course will introduce you to three cutting-edge technologies for privacy-preserving AI: Federated Learning, Differential Privacy, and Encrypted Computation. You will learn how to use the newest privacy-preserving technologies, such as OpenMined’s PySyft. PySyft extends Deep Learning tools—such as PyTorch—with the cryptographic and distributed technologies necessary to safely and securely train AI models on distributed private data.
We encourage you to enter the Secure and Private AI Scholarship Challenge from Facebook to both take the course and have a chance to win a scholarship for the Deep Learning or Computer Vision Nanodegree programs.

Differential Privacy
Federated Learning
Encrypted Computation

Learn the mathematical definition of privacy
Train AI models in PyTorch to learn public information from within private datasets
Train on data that is highly distributed across multiple organizations and data centers using PyTorch and PySyft
Aggregate gradients using a “trusted aggregator”
Do arithmetic on encrypted numbers
Use cryptography to share ownership over a number using Secret Sharing
Leverage Additive Secret Sharing for encrypted Federated Learning

To get the most out of your experience in this course, we recommend the following:
No background in cryptography or advanced mathematics is required.
See the Technology Requirements for using Udacity.

A data scientist can only use AI to solve problems if they have enough training data. Whether you’re at a startup or an enterprise, the most important and valuable problems are problems about people. Solving these problems using AI means having access to a large amount of private and sensitive data.
Want to predict cancer in medical scans? If you’re using traditional Deep Learning tools, this means persuading someone to send you a copy of a sensitive dataset. In many cases, this is either a non-starter or it will severely limit the amount of data you’re allowed to see.
In this course, learn how to apply Deep Learning to private data while maintaining users’ privacy, giving you the ability to train on more data in a privacy-preserving manner so that you can tackle more difficult problems and create smarter, more effective AI models, while also being socially responsible.

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Prerequisites & Facts

Secure and Private AI

Course Topic

Development, Game Development, Unity

University, College, Institution

Udacity

Course Skill Level

Advanced

see course summary

Course Language

English

Place of class

Online, self-paced (see curriculum for more information)

Degree

Certificate

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Degree & Cost

Secure and Private AI

To obtain a verified certificate from Udacity you have to finish this course or the latest version of it, if there is a new edition. The class may be free of charge, but there could be some cost to receive a verified certificate or to access the learning materials. The specifics of the course may have been changed, please consult the provider to get the latest quotes and news.

Udacity

Secure and Private AI

provided by Udacity

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School: Udacity

Topic: Deep Learning

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