Security Audits for Machine Learning Attacks
Speakers Bio:
Navdeep Gill, Lead Data Scientist & Team Lead, Responsible AI at H2O.AI
Navdeep Gill is a Lead Data Scientist, Team Lead at H2O.ai where he leads efforts around Responsible AI and previously focused on GPU accelerated machine learning, automated machine learning, and the core H2O-3 platform.
Prior to joining H2O.ai, Navdeep worked at Cisco focusing on data science and software development. Before that Navdeep was a researcher/analyst in several neuroscience labs at the following institutions: California State University, East Bay, University of California, San Francisco, and Smith Kettlewell Eye Research Institute.
Navdeep graduated from California State University, East Bay with a M.S. in Computational Statistics, a B.S. in Statistics, and a B.A. in Psychology with a minor in Mathematics.
Michelle Canco, Data Scientist at H2O.AI
Michelle is a Data Scientist and Product Manager of the AI App Store at H2O.ai. Her background is in pure math and computer science and she is passionate about applying these skills to answer real world questions. When not coding or thinking of analytics, Michelle can be found hanging out with her dog or playing rocking out with the band.
Abstract:
There are several known attacks against ML models that can lead to altered, harmful model outcomes or exposure of sensitive training data. Unfortunately, traditional model assessment measures don’t tell us much about whether a model is secure. In addition to other debugging steps, it may be prudent to add some or all of the known ML attacks into any white-hat hacking exercises or red-team audits your organization is already conducting. This talk will go over common machine learning security attacks and the remediation steps an organization can take to deter these pitfalls.