1. Moscone West

Despite significant successes, machine learning has serious security and privacy concerns. This talk will examine two of these. First, how adversarial examples can be used to fool state-of-the-art vision classifiers (to, e.g., make self-driving cars incorrectly classify road signs). Second, how to extract private training data out of a trained neural network.

Learning Objectives:
1: Recognize the potential impact of adversarial examples for attacking neural network classifiers.
2: Understand how sensitive training data can be leaked through exposing APIs to pre-trained models.
3: Know when you need to deploy defenses to counter these new threats in the machine learning age.

Pre-Requisites:
Understanding of threats on traditional classifiers (e.g., spam or malware systems), evasion attacks, and privacy, as well as the basics of machine learning.

Participants: