Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Importance of Difficulty Calibration in Membership Inference
  Attacks   The vulnerability of machine learning models to membership inference attacks
has received much attention in recent years. However, existing attacks mostly
remain impractical due to having high false positive rates, where non-member
samples are often erroneously predicted as members. This type of error makes
the predicted membership signal unreliable, especially since most samples are
non-members in real world applications. In this work, we argue that membership
inference attacks can benefit drastically from \emph{difficulty calibration},
where an attack's predicted membership score is adjusted to the difficulty of
correctly classifying the target sample. We show that difficulty calibration
can significantly reduce the false positive rate of a variety of existing
attacks without a loss in accuracy.

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The categories of this paper are: math
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The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
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The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
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The categories of this paper are: eess
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The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
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The categories of this paper are: comp-gas
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The categories of this paper are: mtrl-th
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The categories of this paper are: solv-int
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The categories of this paper are: supr-con
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The categories of this paper are: q-bio
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The categories of this paper are: acc-phys