Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.14792 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913211085750272 |
|---|---|
| author | Razeghi, Behrooz Rahimi, Parsa Marcel, Sébastien |
| author_facet | Razeghi, Behrooz Rahimi, Parsa Marcel, Sébastien |
| contents | In this study, we harness the information-theoretic Privacy Funnel (PF) model to develop a method for privacy-preserving representation learning using an end-to-end training framework. We rigorously address the trade-off between obfuscation and utility. Both are quantified through the logarithmic loss, a measure also recognized as self-information loss. This exploration deepens the interplay between information-theoretic privacy and representation learning, offering substantive insights into data protection mechanisms for both discriminative and generative models. Importantly, we apply our model to state-of-the-art face recognition systems. The model demonstrates adaptability across diverse inputs, from raw facial images to both derived or refined embeddings, and is competent in tasks such as classification, reconstruction, and generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14792 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Variational Privacy Funnel: General Modeling with Applications in Face Recognition Razeghi, Behrooz Rahimi, Parsa Marcel, Sébastien Computer Vision and Pattern Recognition Information Theory Machine Learning In this study, we harness the information-theoretic Privacy Funnel (PF) model to develop a method for privacy-preserving representation learning using an end-to-end training framework. We rigorously address the trade-off between obfuscation and utility. Both are quantified through the logarithmic loss, a measure also recognized as self-information loss. This exploration deepens the interplay between information-theoretic privacy and representation learning, offering substantive insights into data protection mechanisms for both discriminative and generative models. Importantly, we apply our model to state-of-the-art face recognition systems. The model demonstrates adaptability across diverse inputs, from raw facial images to both derived or refined embeddings, and is competent in tasks such as classification, reconstruction, and generation. |
| title | Deep Variational Privacy Funnel: General Modeling with Applications in Face Recognition |
| topic | Computer Vision and Pattern Recognition Information Theory Machine Learning |
| url | https://arxiv.org/abs/2401.14792 |