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Siamese Generative Adversarial Privatizer for Biometric Data

  • Witold OleszkiewiczEmail author
  • Peter Kairouz
  • Karol Piczak
  • Ram Rajagopal
  • Tomasz Trzciński
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11365)

Abstract

State-of-the-art machine learning algorithms can be fooled by carefully crafted adversarial examples. As such, adversarial examples present a concrete problem in AI safety. In this work we turn the tables and ask the following question: can we harness the power of adversarial examples to prevent malicious adversaries from learning identifying information from data while allowing non-malicious entities to benefit from the utility of the same data? For instance, can we use adversarial examples to anonymize biometric dataset of faces while retaining usefulness of this data for other purposes, such as emotion recognition? To address this question, we propose a simple yet effective method, called Siamese Generative Adversarial Privatizer (SGAP), that exploits the properties of a Siamese neural network to find discriminative features that convey identifying information. When coupled with a generative model, our approach is able to correctly locate and disguise identifying information, while minimally reducing the utility of the privatized dataset. Extensive evaluation on a biometric dataset of fingerprints and cartoon faces confirms usefulness of our simple yet effective method.

Notes

Acknowledgment

The work was partially supported as RENOIR Project by the European Union Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 691152 (project RENOIR) and by Ministry of Science and Higher Education (Poland), grant No. W34/H2020/2016. We thank NVIDIA Corporation for donating Titan Xp GPU that was used for this research.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Witold Oleszkiewicz
    • 1
    Email author
  • Peter Kairouz
    • 2
  • Karol Piczak
    • 1
  • Ram Rajagopal
    • 2
  • Tomasz Trzciński
    • 1
    • 3
  1. 1.Warsaw University of TechnologyWarsawPoland
  2. 2.Stanford UniversityStanfordUSA
  3. 3.TooplooxWrocławPoland

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