About: Privacy Verification of PhotoDNA Based on Machine Learning

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type	http://eprints.org/ontology/BookSectionEPrint http://eprints.org/ontology/BookSectionEPrint http://eprints.org/ontology/EPrint http://eprints.org/ontology/EPrint Article Article Book Section Book Section
seeAlso	HTML Summary of #77165 Privacy Verification of PhotoDNA Based on Machine Learning HTML Summary of #77165 Privacy Verification of PhotoDNA Based on Machine Learning
sameAs	Privacy Verification of PhotoDNA Based on Machine Learning Privacy Verification of PhotoDNA Based on Machine Learning
http://eprints.org/ontology/hasDocument	Privacy Verification of PhotoDNA Based on Machine Learning (PDF) Privacy Verification of PhotoDNA Based on Machine Learning (PDF) Privacy Verification of PhotoDNA Based on Machine Learning (Other) Privacy Verification of PhotoDNA Based on Machine Learning (Other) Privacy Verification of PhotoDNA Based on Machine Learning (Other) Privacy Verification of PhotoDNA Based on Machine Learning (Other) Privacy Verification of PhotoDNA Based on Machine Learning (Other) Privacy Verification of PhotoDNA Based on Machine Learning (Other) Privacy Verification of PhotoDNA Based on Machine Learning (Other) Privacy Verification of PhotoDNA Based on Machine Learning (Other) Privacy Verification of PhotoDNA Based on Machine Learning (Other) Privacy Verification of PhotoDNA Based on Machine Learning (Other)
http://eprints.org/ontology/hasSubmitted	Privacy Verification of PhotoDNA Based on Machine Learning (PDF) Privacy Verification of PhotoDNA Based on Machine Learning (PDF)
dc:hasVersion	Privacy Verification of PhotoDNA Based on Machine Learning (PDF) Privacy Verification of PhotoDNA Based on Machine Learning (PDF)
Title	Privacy Verification of PhotoDNA Based on Machine Learning Privacy Verification of PhotoDNA Based on Machine Learning
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2019-08-31 2019-08-31
Creator	Muhammad Shahroz Nadeem Muhammad Shahroz Nadeem Virginia N. L. Franqueira Virginia N. L. Franqueira Xiaojun Zhai Xiaojun Zhai
status	peer reviewed peer reviewed published published
Publisher	IET IET
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. 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.
Is Part Of	https://kar.kent.ac.uk/id/repository https://kar.kent.ac.uk/id/repository
list of authors	https://kar.kent.ac.uk/id/eprint/77165#authors https://kar.kent.ac.uk/id/eprint/77165#authors
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #77165 Privacy Verification of PhotoDNA Based on Machine Learning HTML Summary of #77165 Privacy Verification of PhotoDNA Based on Machine Learning

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