Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Privacy-preserving Neural Representations of Text   This article deals with adversarial attacks towards deep learning systems for
Natural Language Processing (NLP), in the context of privacy protection. We
study a specific type of attack: an attacker eavesdrops on the hidden
representations of a neural text classifier and tries to recover information
about the input text. Such scenario may arise in situations when the
computation of a neural network is shared across multiple devices, e.g. some
hidden representation is computed by a user's device and sent to a cloud-based
model. We measure the privacy of a hidden representation by the ability of an
attacker to predict accurately specific private information from it and
characterize the tradeoff between the privacy and the utility of neural
representations. Finally, we propose several defense methods based on modified
training objectives and show that they improve the privacy of neural
representations.

35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
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The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ