Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Membership Inference Attacks From First Principles   A membership inference attack allows an adversary to query a trained machine
learning model to predict whether or not a particular example was contained in
the model's training dataset. These attacks are currently evaluated using
average-case "accuracy" metrics that fail to characterize whether the attack
can confidently identify any members of the training set. We argue that attacks
should instead be evaluated by computing their true-positive rate at low (e.g.,
<0.1%) false-positive rates, and find most prior attacks perform poorly when
evaluated in this way. To address this we develop a Likelihood Ratio Attack
(LiRA) that carefully combines multiple ideas from the literature. Our attack
is 10x more powerful at low false-positive rates, and also strictly dominates
prior attacks on existing metrics.

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The categories of this paper are: acc-phys
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The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
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The categories of this paper are: mtrl-th
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The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
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The categories of this paper are: supr-con
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The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat