March 7, 2022

Augmenting Privacy with AI

March 7, 2022  •  1 min  • 82 words  •


Relevant Projects


    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.


    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.
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