Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Auditing Differentially Private Machine Learning: How Private is Private
  SGD?   We investigate whether Differentially Private SGD offers better privacy in
practice than what is guaranteed by its state-of-the-art analysis. We do so via
novel data poisoning attacks, which we show correspond to realistic privacy
attacks. While previous work (Ma et al., arXiv 2019) proposed this connection
between differential privacy and data poisoning as a defense against data
poisoning, our use as a tool for understanding the privacy of a specific
mechanism is new. More generally, our work takes a quantitative, empirical
approach to understanding the privacy afforded by specific implementations of
differentially private algorithms that we believe has the potential to
complement and influence analytical work on differential privacy.

30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn