Augmenting Privacy with AI
March 7, 2022 • 1 min • 82 words •
Information
Relevant Projects
Confidant
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Little is known about how robots might appropriately discern the sensitivity of information, which has major implications for human-robot trust. As a first step, we designed a privacy controller, CONFIDANT, for conversational social robots, capable of using contextual metadata from conversations to model privacy boundaries.
Face-Off
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Advances in deep learning have made face recognition technologies and surveillance pervasive. Face-Off is a privacy-preserving framework that introduces strategic perturbations to the user's face to prevent it from being correctly recognized.
