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    Privacy verification of photoDNA based on machine learning

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    Authors
    Nadeem, Muhammad Shahroz
    Franqueira, Virginia N. L. cc
    Zhai, Xiaojun cc
    Affiliation
    University of Derby, College of Engineering and Technology
    University of Essex, School of Computer Science and Electronic Engineering
    Issue Date
    2019-10-09
    
    Metadata
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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.
    Citation
    Nadeem, M. S., Franqueira, V. N. L. and Zhai, X (2019). 'Privacy verification of photoDNA based on machine learning', In Ren, W., Wang, L., Choo, K. K. R. and Xhafa F. (eds.) 'Security and privacy for big data, cloud computing and applications'. Hertfordshire: The Institution of Engineering and Technology, pp. 263-280.
    Publisher
    The Institution of Engineering and Technology (IET)
    URI
    http://hdl.handle.net/10545/624203
    Additional Links
    https://shop.theiet.org/security-and-privacy-for-big-data-cloud-computing-and-applications
    Type
    Book chapter
    Language
    en
    ISBN
    9781785617478
    Collections
    Department of Electronics, Computing & Maths

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