Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Achieving differential privacy for $k$-nearest neighbors based outlier
  detection by data partitioning   When applying outlier detection in settings where data is sensitive,
mechanisms which guarantee the privacy of the underlying data are needed. The
$k$-nearest neighbors ($k$-NN) algorithm is a simple and one of the most
effective methods for outlier detection. So far, there have been no attempts
made to develop a differentially private ($\epsilon$-DP) approach for $k$-NN
based outlier detection. Existing approaches often relax the notion of
$\epsilon$-DP and employ other methods than $k$-NN. We propose a method for
$k$-NN based outlier detection by separating the procedure into a fitting step
on reference inlier data and then apply the outlier classifier to new data. We
achieve $\epsilon$-DP for both the fitting algorithm and the outlier classifier
with respect to the reference data by partitioning the dataset into a uniform
grid, which yields low global sensitivity. Our approach yields nearly optimal
performance on real-world data with varying dimensions when compared to the
non-private versions of $k$-NN.

13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org