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Training privacy-preserving video analytics pipelines by suppressing features that reveal information about private attributes. (arXiv:2203.02635v2 [cs.CV] UPDATED)
June 3, 2022, 1:20 a.m. | Chau Yi Li, Andrea Cavallaro
cs.CR updates on arXiv.org arxiv.org
Deep neural networks are increasingly deployed for scene analytics, including
to evaluate the attention and reaction of people exposed to out-of-home
advertisements. However, the features extracted by a deep neural network that
was trained to predict a specific, consensual attribute (e.g. emotion) may also
encode and thus reveal information about private, protected attributes (e.g.
age or gender). In this work, we focus on such leakage of private information
at inference time. We consider an adversary with access to the features …
analytics attributes features information privacy training video
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