Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Privacy Amplification via Shuffling for Linear Contextual Bandits   Contextual bandit algorithms are widely used in domains where it is desirable
to provide a personalized service by leveraging contextual information, that
may contain sensitive information that needs to be protected. Inspired by this
scenario, we study the contextual linear bandit problem with differential
privacy (DP) constraints. While the literature has focused on either
centralized (joint DP) or local (local DP) privacy, we consider the shuffle
model of privacy and we show that is possible to achieve a privacy/utility
trade-off between JDP and LDP. By leveraging shuffling from privacy and
batching from bandits, we present an algorithm with regret bound
$\widetilde{\mathcal{O}}(T^{2/3}/\varepsilon^{1/3})$, while guaranteeing both
central (joint) and local privacy. Our result shows that it is possible to
obtain a trade-off between JDP and LDP by leveraging the shuffle model while
preserving local privacy.

12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc