Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SHAPr: An Efficient and Versatile Membership Privacy Risk Metric for
  Machine Learning   Data used to train machine learning (ML) models can be sensitive. Membership
inference attacks (MIAs), attempting to determine whether a particular data
record was used to train an ML model, risk violating membership privacy. ML
model builders need a principled definition of a metric that enables them to
quantify the privacy risk of (a) individual training data records, (b)
independently of specific MIAs, (c) efficiently. None of the prior work on
membership privacy risk metrics simultaneously meets all of these criteria.
  We propose such a metric, SHAPr, which uses Shapley values to quantify a
model's memorization of an individual training data record by measuring its
influence on the model's utility. This memorization is a measure of the
likelihood of a successful MIA.
  Using ten benchmark datasets, we show that SHAPr is effective (precision:
0.94$\pm 0.06$, recall: 0.88$\pm 0.06$) in estimating susceptibility of a
training data record for MIAs, and is efficient (computable within minutes for
smaller datasets and in ~90 minutes for the largest dataset).
  SHAPr is also versatile in that it can be used for other purposes like
assessing fairness or assigning valuation for subsets of a dataset. For
example, we show that SHAPr correctly captures the disproportionate
vulnerability of different subgroups to MIAs.
  Using SHAPr, we show that the membership privacy risk of a dataset is not
necessarily improved by removing high risk training data records, thereby
confirming an observation from prior work in a significantly extended setting
(in ten datasets, removing up to 50% of data).

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The categories of this paper are: gr-qc
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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: supr-con
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The categories of this paper are: hep-lat
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The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys
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The categories of this paper are: nlin
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The categories of this paper are: solv-int
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The categories of this paper are: hep-th
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The categories of this paper are: econ
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The categories of this paper are: nucl-ex
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The categories of this paper are: physics
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The categories of this paper are: q-bio
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The categories of this paper are: chem-ph