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Privacy Verification of PhotoDNA Based on Machine Learning

Nadeem, Muhammad Shahroz and Franqueira, Virginia N. L. and Zhai, Xiaojun (2019) Privacy Verification of PhotoDNA Based on Machine Learning. In: Security and Privacy for Big Data, Cloud Computing and Applications. IET, pp. 263-280. E-ISBN 978-1-78561-748-5. (doi:10.1049/PBPC028E_ch12) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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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.

Item Type: Book section
DOI/Identification number: 10.1049/PBPC028E_ch12
Additional information: I've uploaded the final proof of the chapter.
Uncontrolled keywords: PhotoDNA, Fuzzy Hashing, Privacy-preserving technology, Machine learning, Evaluation.
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Security Group
Depositing User: Virginia Nunes Leal Franqueira
Date Deposited: 16 Oct 2019 10:34 UTC
Last Modified: 21 Oct 2019 09:30 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/77165 (The current URI for this page, for reference purposes)
Nadeem, Muhammad Shahroz: https://orcid.org/0000-0001-5835-1602
Franqueira, Virginia N. L.: https://orcid.org/0000-0003-1332-9115
Zhai, Xiaojun: https://orcid.org/0000-0002-1030-8311
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