Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Wishart Mechanism for Differentially Private Principal Components
  Analysis   We propose a new input perturbation mechanism for publishing a covariance
matrix to achieve $(\epsilon,0)$-differential privacy. Our mechanism uses a
Wishart distribution to generate matrix noise. In particular, We apply this
mechanism to principal component analysis. Our mechanism is able to keep the
positive semi-definiteness of the published covariance matrix. Thus, our
approach gives rise to a general publishing framework for input perturbation of
a symmetric positive semidefinite matrix. Moreover, compared with the classic
Laplace mechanism, our method has better utility guarantee. To the best of our
knowledge, Wishart mechanism is the best input perturbation approach for
$(\epsilon,0)$-differentially private PCA. We also compare our work with
previous exponential mechanism algorithms in the literature and provide near
optimal bound while having more flexibility and less computational
intractability.

31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int