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      Key protected classification for collaborative learning

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      Embargo Lift Date: 2022-08-01
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      Author(s)
      Sarıyıldız, Mert Bülent
      Cinbiş, R. G.
      Ayday, Erman
      Date
      2020
      Source Title
      Pattern Recognition
      Print ISSN
      0031-3203
      Publisher
      Elsevier
      Volume
      104
      Pages
      1 - 13
      Language
      English
      Type
      Article
      Item Usage Stats
      50
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      Abstract
      Large-scale datasets play a fundamental role in training deep learning models. However, dataset collection is difficult in domains that involve sensitive information. Collaborative learning techniques provide a privacy-preserving solution, by enabling training over a number of private datasets that are not shared by their owners. However, recently, it has been shown that the existing collaborative learning frameworks are vulnerable to an active adversary that runs a generative adversarial network (GAN) attack. In this work, we propose a novel classification model that is resilient against such attacks by design. More specifically, we introduce a key-based classification model and a principled training scheme that protects class scores by using class-specific private keys, which effectively hide the information necessary for a GAN attack. We additionally show how to utilize high dimensional keys to improve the robustness against attacks without increasing the model complexity. Our detailed experiments demonstrate the effectiveness of the proposed technique. Source code will be made available at https://github.com/mbsariyildiz/key-protected-classification.
      Keywords
      Privacy-preserving machine learning
      Collaborative learning
      Classification
      Generative adversarial networks
      Permalink
      http://hdl.handle.net/11693/75817
      Published Version (Please cite this version)
      https://dx.doi.org/10.1016/j.patcog.2020.107327
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      • Department of Computer Engineering 1510
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