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Privacy Safe Representation Learning via Frequency Filtering Encoder. (arXiv:2208.02482v1 [cs.CV])
Aug. 5, 2022, 1:20 a.m. | Jonghu Jeong, Minyong Cho, Philipp Benz, Jinwoo Hwang, Jeewook Kim, Seungkwan Lee, Tae-hoon Kim
cs.CR updates on arXiv.org arxiv.org
Deep learning models are increasingly deployed in real-world applications.
These models are often deployed on the server-side and receive user data in an
information-rich representation to solve a specific task, such as image
classification. Since images can contain sensitive information, which users
might not be willing to share, privacy protection becomes increasingly
important. Adversarial Representation Learning (ARL) is a common approach to
train an encoder that runs on the client-side and obfuscates an image. It is
assumed, that the obfuscated …
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