Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Winning the NIST Contest: A scalable and general approach to
  differentially private synthetic data   We propose a general approach for differentially private synthetic data
generation, that consists of three steps: (1) select a collection of
low-dimensional marginals, (2) measure those marginals with a noise addition
mechanism, and (3) generate synthetic data that preserves the measured
marginals well. Central to this approach is Private-PGM, a post-processing
method that is used to estimate a high-dimensional data distribution from noisy
measurements of its marginals. We present two mechanisms, NIST-MST and MST,
that are instances of this general approach. NIST-MST was the winning mechanism
in the 2018 NIST differential privacy synthetic data competition, and MST is a
new mechanism that can work in more general settings, while still performing
comparably to NIST-MST. We believe our general approach should be of broad
interest, and can be adopted in future mechanisms for synthetic data
generation.

17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph