Face image anonymization as an application of multidimensional data K-anonymizer

Taichi Nakamura, Yuiko Sakuma, Hiroaki Nishi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently, the development of data communication networks and advances in computer processing capacity have significantly increased the amount of data to be managed. Although future innovations using data are expected, privacy violation of data has become a problem. For example, image disclosure in Social Networking Service may result in infringement of portrait rights. Previously, studies on anonymization have been conducted to disclose data for protecting personal information. However, conventional anonymization, even for high-dimensional data such as faced images, uses an averaging operation in anonymization and does not consider complex relationships between dimensions. Therefore, the loss of semantic meaning increases because it is caused by the anonymization process, assuming an Euclidean data space. We herein propose Multidimensional Inputs K-anonymizing Unit(MIKU), an anonymization algorithm focusing on face image anonymization as a typical example of high-dimensional datMIKU enables anonymization that retains the image quality. As the comparison method directly anonymizes images, the quality of images is deteriorated. MIKU considers the relation between dimensions using the latent space of StyleGAN. The effect of using latent space was confirmed by comparing the quantitative results of Fréchet Inception Distance(FID) and the qualitative evaluation of the output image with the comparison method. The quantitative results by FID proved that MIKU achieved better performance when K-anonymity was large. When K-anonymity is small, we clarified that the image quality of the comparison method such as FID could not be measured correctly and was overestimated because of the effect of the inception model's linearity. In addition, the qualitative evaluation of anonymized images shows that MIKU generates a more natural image in the contour and hair expressions than the comparison method, and the anonymized images of MIKU contain no unnatural edge lines on a face such as those generated in the comparison method.

Original languageEnglish
Title of host publicationProceedings - 2019 7th International Symposium on Computing and Networking Workshops, CANDARW 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages155-161
Number of pages7
ISBN (Electronic)9781728152684
DOIs
Publication statusPublished - 2019 Nov
Event7th International Symposium on Computing and Networking Workshops, CANDARW 2019 - Nagasaki, Japan
Duration: 2019 Nov 262019 Nov 29

Publication series

NameProceedings - 2019 7th International Symposium on Computing and Networking Workshops, CANDARW 2019

Conference

Conference7th International Symposium on Computing and Networking Workshops, CANDARW 2019
CountryJapan
CityNagasaki
Period19/11/2619/11/29

Keywords

  • Face Image
  • High-Dimension
  • K-Anonymization
  • Neural Network
  • StyleGAN

ASJC Scopus subject areas

  • Hardware and Architecture
  • Information Systems
  • Artificial Intelligence
  • Computer Networks and Communications

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