Booz Allen Hamilton Colloquium: Machine Learning with Differential Privacy, Roxana Geambasu

Friday, October 25, 2019
3:30 p.m.-4:30 p.m.
1110 Kim Engineering Building
Kara Stamets
301 405 4471
stametsk@umd.edu

Security and Privacy Guarantees in Machine Learning with Differential Privacy

Roxana Geambasu,
Associate Professor of Computer Science,
Columbia University 

Abstract: Machine learning (ML) is driving many of our applications and life-changing decisions.  Yet, it is often brittle and unstable, making decisions that are hard to understand or can be exploited.  Tiny changes to an input can cause dramatic changes in predictions; this results in decisions that surprise, appear unfair, or enable attack vectors such as adversarial examples.  Moreover, models trained on users' data can encode not only general trends from large datasets but also very specific, personal information from these datasets, such as social security numbers and credit card numbers from emails; this threatens to expose users' secrets through ML models or predictions.  This talk positions differential privacy (DP) -- a rigorous privacy theory -- as a versatile foundation for building into ML much-needed guarantees of not only privacy but also of security and stability.  I first present PixelDP (S&P'19), a scalable certified defense against adversarial examples that leverages DP theory to guarantee a level of robustness against this attack.  I then present Sage (SOSP'19), a DP ML platform that bounds the cumulative leakage of secrets through models while addressing some of the most pressing challenges of DP, such as running out of privacy budget and the privacy-accuracy tradeoff.  PixelDP and Sage are designed from a pragmatic systems perspective and illustrate that DP theory is powerful but requires adaptation to achieve practical guarantees for ML workloads.

Bio: Roxana Geambasu is an Associate Professor of Computer Science at Columbia University and a member of Columbia's Data Sciences Institute. She joined Columbia in Fall 2011 after finishing her Ph.D. at the University of Washington.  For her work in cloud and mobile data privacy, she received: an Alfred P. Sloan Faculty Fellowship, an NSF CAREER award, a Microsoft Research Faculty Fellowship, several Google Faculty awards, a "Brilliant 10" Popular Science nomination, the Honorable Mention for the 2013 inaugural Dennis M. Ritchie Doctoral Dissertation Award, a William Chan Dissertation Award, two best paper awards at top systems conferences, and the first Google Ph.D. Fellowship in Cloud Computing.

Audience: Clark School  Graduate  Undergraduate  Faculty  Post-Docs  Alumni 

 

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