A Study on Image Privacy Protection in Response to Artificial Intelligence Technology

Xue, Hanyu

A Study on Image Privacy Protection in Response to Artificial Intelligence Technology

Xue, Hanyu

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Publication Type:: Thesis
Issue Date:: 2023

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Field	Value	Language
dc.contributor.author	Xue, Hanyu
dc.date.accessioned	2024-04-15T23:42:37Z
dc.date.available	2024-04-15T23:42:37Z
dc.date.issued	2023
dc.identifier.uri	http://hdl.handle.net/10453/177927
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	This thesis addresses image privacy in the era of data sharing and artificial intelligence (AI). Motivated by AI-induced privacy risks, it aims to develop a practical framework and assess its methods. Challenges include inadequate defence against AI, limited privacy definitions, and the need for a privacy-utility balance. Contributions include a social media image privacy framework using adversarial perturbations, preserving utility while hiding private information. A differentially private image (DP-Image) framework perturbs feature vectors, ensuring privacy against human and AI adversaries. User centric privacy protection allows customizable levels, empowering users in computer vision applications. The thesis tackles high-quality image de-identification, enhancing anonymized datasets with decoder and attribute optimization techniques. Overall, it significantly advances social media privacy, provable privacy protection, user-centric privacy, and high-quality image de-identification in the age of data sharing and AI.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/177927/1/thesis.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2023 Hanyu Xue
dc.rights	au.edu.uts.lib/cph
dc.title	A Study on Image Privacy Protection in Response to Artificial Intelligence Technology	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

This thesis addresses image privacy in the era of data sharing and artificial intelligence (AI). Motivated by AI-induced privacy risks, it aims to develop a practical framework and assess its methods. Challenges include inadequate defence against AI, limited privacy definitions, and the need for a privacy-utility balance. Contributions include a social media image privacy framework using adversarial perturbations, preserving utility while hiding private information. A differentially private image (DP-Image) framework perturbs feature vectors, ensuring privacy against human and AI adversaries. User centric privacy protection allows customizable levels, empowering users in computer vision applications. The thesis tackles high-quality image de-identification, enhancing anonymized datasets with decoder and attribute optimization techniques. Overall, it significantly advances social media privacy, provable privacy protection, user-centric privacy, and high-quality image de-identification in the age of data sharing and AI.

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http://hdl.handle.net/10453/177927