Keke Chen
Associate Professor
CSEE Department
UMBC
1pm–2pm [note the exceptional time]
Friday, November 1, 2024
Remotely via WebEx: https://umbc.webex.com/meet/sherman
Abstract:
I will briefly discuss our demo system recently accepted by CCS 2024 that estimates privacy risks of participating in a
machine learning task. As many AI models depend on sensitive training data (e.g., identifiable images and text
conversations), privacy has increasingly been a major concern in the AI era. Methods like differential privacy allow users
to quantify the acceptable privacy loss, which may also lead to significant utility loss. As a result, controlled data access is
still the mainstream method for protecting data privacy or controlling privacy leakage in industrial and research settings.
However, there is no quantitative measure for individual data contributors to tell their privacy risks before participating in
a machine learning task. We developed the demo prototype FT-PrivacyScore to efficiently and quantitatively estimate the
privacy risk of participating in a model fine-tuning task. With FT-PrivacyScore, participants and data consumers can
prepare proactively to ensure better protected privacy.
About the Speaker:
Dr. Keke Chen is an associate professor in the CSEE Department at UMBC. His recent research focuses on privacy and
security issues with AI model training and deployment. He earned his Ph.D. in computer science from Georgia Tech in
2006. Before joining UMBC, he was a Northwestern Mutual associate professor of computer science at Marquette
University. Email: kekechen@umbc.edu
Host:
Alan T. Sherman, sherman@umbc.edu
Support for this event was provided in part by the National Science Foundation under SFS grant DGE-1753681.
The UMBC Cyber Defense Lab meets biweekly Fridays 12-1pm. All meetings are open to the public.
Upcoming CDL meetings:
November 15, Houbing Song (IS, UMBC)
December 6, Zhiuan Chen (IS, UMBC), Privacy