Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Private Approximations of the 2nd-Moment Matrix Using Existing
  Techniques in Linear Regression   We introduce three differentially-private algorithms that approximates the
2nd-moment matrix of the data. These algorithm, which in contrast to existing
algorithms output positive-definite matrices, correspond to existing techniques
in linear regression literature. Specifically, we discuss the following three
techniques. (i) For Ridge Regression, we propose setting the regularization
coefficient so that by approximating the solution using Johnson-Lindenstrauss
transform we preserve privacy. (ii) We show that adding a small batch of random
samples to our data preserves differential privacy. (iii) We show that sampling
the 2nd-moment matrix from a Bayesian posterior inverse-Wishart distribution is
differentially private provided the prior is set correctly. We also evaluate
our techniques experimentally and compare them to the existing "Analyze Gauss"
algorithm of Dwork et al.

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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-th
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
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The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
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The categories of this paper are: nlin
21
The categories of this paper are: adap-org
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The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
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The categories of this paper are: patt-sol