Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: TableGAN-MCA: Evaluating Membership Collisions of GAN-Synthesized
  Tabular Data Releasing   Generative Adversarial Networks (GAN)-synthesized table publishing lets
people privately learn insights without access to the private table. However,
existing studies on Membership Inference (MI) Attacks show promising results on
disclosing membership of training datasets of GAN-synthesized tables. Different
from those works focusing on discovering membership of a given data point, in
this paper, we propose a novel Membership Collision Attack against GANs
(TableGAN-MCA), which allows an adversary given only synthetic entries randomly
sampled from a black-box generator to recover partial GAN training data.
Namely, a GAN-synthesized table immune to state-of-the-art MI attacks is
vulnerable to the TableGAN-MCA. The success of TableGAN-MCA is boosted by an
observation that GAN-synthesized tables potentially collide with the training
data of the generator.
  Our experimental evaluations on TableGAN-MCA have five main findings. First,
TableGAN-MCA has a satisfying training data recovery rate on three commonly
used real-world datasets against four generative models. Second, factors,
including the size of GAN training data, GAN training epochs and the number of
synthetic samples available to the adversary, are positively correlated to the
success of TableGAN-MCA. Third, highly frequent data points have high risks of
being recovered by TableGAN-MCA. Fourth, some unique data are exposed to
unexpected high recovery risks in TableGAN-MCA, which may attribute to GAN's
generalization. Fifth, as expected, differential privacy, without the
consideration of the correlations between features, does not show commendable
mitigation effect against the TableGAN-MCA. Finally, we propose two mitigation
methods and show promising privacy and utility trade-offs when protecting
against TableGAN-MCA.

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The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
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The categories of this paper are: physics
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The categories of this paper are: comp-gas
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The categories of this paper are: math
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The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
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The categories of this paper are: hep-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: funct-an
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
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The categories of this paper are: nlin
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The categories of this paper are: q-alg