Privacy verification of photoDNA based on machine learning

Book chapter


Nadeem, Muhammad Shahroz, Franqueira, Virginia N. L. and Zhai, Xiaojun 2019. Privacy verification of photoDNA based on machine learning. in: The Institution of Engineering and Technology (IET).
AuthorsNadeem, Muhammad Shahroz, Franqueira, Virginia N. L. and Zhai, Xiaojun
Abstract

PhotoDNA is a perceptual fuzzy hash technology designed and developed by Microsoft. It is deployed by all major big data service providers to detect Indecent Images of Children (IIOC). Protecting the privacy of individuals is of paramount importance in such images. Microsoft claims that a PhotoDNA hash cannot be reverse engineered into the original image; therefore, it is not possible to identify individuals or objects depicted in the image. In this chapter, we evaluate the privacy protection capability of PhotoDNA by testing it against machine learning. Specifically, our aim is to detect the presence of any structural information that might be utilized to compromise the privacy of the individuals via classification. Due to the widespread usage of PhotoDNA as a deterrent to IIOC by big data companies, ensuring its ability to protect privacy would be crucial. In our experimentation, we achieved a classification accuracy of 57.20%.This result indicates that PhotoDNA is resistant to machine-learning-based classification attacks.

KeywordsPrivacy; PhotoDNA; Machine Learning; Security
Year2019
PublisherThe Institution of Engineering and Technology (IET)
ISBN9781785617478
Web address (URL)http://hdl.handle.net/10545/624203
hdl:10545/624203
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Publication dates09 Oct 2019
Publication process dates
Deposited08 Oct 2019, 07:55
AcceptedMar 2019
ContributorsUniversity of Derby, College of Engineering and Technology and University of Essex, School of Computer Science and Electronic Engineering
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