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Image Pixelization with Differential Privacy

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10980)

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.

Keywords

Image privacy Differential privacy 

Notes

Acknowledgments

This research has been funded by NSF grant CRII-1755884 and a UAlbany FRAP-A Award. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of any of the sponsors such as NSF.

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Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  1. 1.University at Albany, SUNYAlbanyUSA

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