Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PEARL: Data Synthesis via Private Embeddings and Adversarial
  Reconstruction Learning   We propose a new framework of synthesizing data using deep generative models
in a differentially private manner. Within our framework, sensitive data are
sanitized with rigorous privacy guarantees in a one-shot fashion, such that
training deep generative models is possible without re-using the original data.
Hence, no extra privacy costs or model constraints are incurred, in contrast to
popular approaches such as Differentially Private Stochastic Gradient Descent
(DP-SGD), which, among other issues, causes degradation in privacy guarantees
as the training iteration increases. We demonstrate a realization of our
framework by making use of the characteristic function and an adversarial
re-weighting objective, which are of independent interest as well. Our proposal
has theoretical guarantees of performance, and empirical evaluations on
multiple datasets show that our approach outperforms other methods at
reasonable levels of privacy.

24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas