Privacy-Preserving Active Learning
Details
Washington, DC USA. Date of Talk: 2017-07-11
Event
Privacy-Preserving Active Learning
Our goal is to drive the deployment of a privacy-respecting screening capability to detect individuals, behaviors, areas, or data samples of high interest. We will deliver a technical specification of a privacy-preserving active learning system and provide a demonstration of a part of its use. The technical approach is to use differentially private mechanisms to perturb classifier parameters extracted from a data stream in order to prevent disclosure of sensitive attributes in that data stream. This work is done in collaboration with Dr. Rebecca Wright and Daniel Bittner of Rutgers University.
Additional information
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