Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Lightweight Privacy-Preserving Scheme Using Label-based Pixel Block
  Mixing for Image Classification in Deep Learning   To ensure the privacy of sensitive data used in the training of deep learning
models, a number of privacy-preserving methods have been designed by the
research community. However, existing schemes are generally designed to work
with textual data, or are not efficient when a large number of images is used
for training. Hence, in this paper we propose a lightweight and efficient
approach to preserve image privacy while maintaining the availability of the
training set. Specifically, we design the pixel block mixing algorithm for
image classification privacy preservation in deep learning. To evaluate its
utility, we use the mixed training set to train the ResNet50, VGG16,
InceptionV3 and DenseNet121 models on the WIKI dataset and the CNBC face
dataset. Experimental findings on the testing set show that our scheme
preserves image privacy while maintaining the availability of the training set
in the deep learning models. Additionally, the experimental results demonstrate
that we achieve good performance for the VGG16 model on the WIKI dataset and
both ResNet50 and DenseNet121 on the CNBC dataset. The pixel block algorithm
achieves fairly high efficiency in the mixing of the images, and it is
computationally challenging for the attackers to restore the mixed training set
to the original training set. Moreover, data augmentation can be applied to the
mixed training set to improve the training's effectiveness.

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The categories of this paper are: quant-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
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The categories of this paper are: math-ph
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The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
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The categories of this paper are: q-bio
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The categories of this paper are: astro-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: stat
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The categories of this paper are: q-fin
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The categories of this paper are: adap-org
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The categories of this paper are: nlin
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The categories of this paper are: dg-ga
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The categories of this paper are: mtrl-th
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The categories of this paper are: supr-con