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Image Pixelization with Differential Privacy

Abstract : Ubiquitous surveillance cameras and personal devices have given rise to the vast generation of image data. While sharing the image data can benefit various applications, including intelligent transportation systems and social science research, those images may capture sensitive individual information, such as license plates, identities, etc. Existing image privacy preservation techniques adopt deterministic obfuscation, e.g., pixelization, which can lead to re-identification with well-trained neural networks. In this study, we propose sharing pixelized images with rigorous privacy guarantees. We extend the standard differential privacy notion to image data, which protects individuals, objects, or their features. Empirical evaluation with real-world datasets demonstrates the utility and efficiency of our method; despite its simplicity, our method is shown to effectively reduce the success rate of re-identification attacks.
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Submitted on : Thursday, December 13, 2018 - 4:04:00 PM
Last modification on : Tuesday, August 13, 2019 - 11:00:07 AM
Long-term archiving on: : Thursday, March 14, 2019 - 3:31:59 PM

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Liyue Fan. Image Pixelization with Differential Privacy. 32th IFIP Annual Conference on Data and Applications Security and Privacy (DBSec), Jul 2018, Bergamo, Italy. pp.148-162, ⟨10.1007/978-3-319-95729-6_10⟩. ⟨hal-01954420⟩

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