%0 Conference Proceedings
%T Image Pixelization with Differential Privacy
%+ State University of New York (SUNY)
%A Fan, Liyue
%Z Part 3: Privacy-Preserving Access and Computation
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 32th IFIP Annual Conference on Data and Applications Security and Privacy (DBSec)
%C Bergamo, Italy
%Y Florian Kerschbaum
%Y Stefano Paraboschi
%I Springer International Publishing
%3 Data and Applications Security and Privacy XXXII
%V LNCS-10980
%P 148-162
%8 2018-07-16
%D 2018
%R 10.1007/978-3-319-95729-6_10
%K Image privacy
%K Differential privacy
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 11
%Z WG 11.3
%2 https://inria.hal.science/hal-01954420/document
%2 https://inria.hal.science/hal-01954420/file/470961_1_En_10_Chapter.pdf
%L hal-01954420
%U https://inria.hal.science/hal-01954420
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC11
%~ IFIP-WG11-3
%~ IFIP-DBSEC
%~ IFIP-LNCS-10980