AnonyNoise: Anonymizing Event Data with Smart Noise to Outsmart Re-Identification and Preserve Privacy
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913585269047296 |
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| author | Bendig, Katharina Schuster, René Thiemer, Nicole Joisten, Karen Stricker, Didier |
| author_facet | Bendig, Katharina Schuster, René Thiemer, Nicole Joisten, Karen Stricker, Didier |
| contents | The increasing capabilities of deep neural networks for re-identification, combined with the rise in public surveillance in recent years, pose a substantial threat to individual privacy. Event cameras were initially considered as a promising solution since their output is sparse and therefore difficult for humans to interpret. However, recent advances in deep learning proof that neural networks are able to reconstruct high-quality grayscale images and re-identify individuals using data from event cameras. In our paper, we contribute a crucial ethical discussion on data privacy and present the first event anonymization pipeline to prevent re-identification not only by humans but also by neural networks. Our method effectively introduces learnable data-dependent noise to cover personally identifiable information in raw event data, reducing attackers' re-identification capabilities by up to 60%, while maintaining substantial information for the performing of downstream tasks. Moreover, our anonymization generalizes well on unseen data and is robust against image reconstruction and inversion attacks. Code: https://github.com/dfki-av/AnonyNoise |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_16440 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AnonyNoise: Anonymizing Event Data with Smart Noise to Outsmart Re-Identification and Preserve Privacy Bendig, Katharina Schuster, René Thiemer, Nicole Joisten, Karen Stricker, Didier Computer Vision and Pattern Recognition The increasing capabilities of deep neural networks for re-identification, combined with the rise in public surveillance in recent years, pose a substantial threat to individual privacy. Event cameras were initially considered as a promising solution since their output is sparse and therefore difficult for humans to interpret. However, recent advances in deep learning proof that neural networks are able to reconstruct high-quality grayscale images and re-identify individuals using data from event cameras. In our paper, we contribute a crucial ethical discussion on data privacy and present the first event anonymization pipeline to prevent re-identification not only by humans but also by neural networks. Our method effectively introduces learnable data-dependent noise to cover personally identifiable information in raw event data, reducing attackers' re-identification capabilities by up to 60%, while maintaining substantial information for the performing of downstream tasks. Moreover, our anonymization generalizes well on unseen data and is robust against image reconstruction and inversion attacks. Code: https://github.com/dfki-av/AnonyNoise |
| title | AnonyNoise: Anonymizing Event Data with Smart Noise to Outsmart Re-Identification and Preserve Privacy |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.16440 |