Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploiting Unintended Feature Leakage in Collaborative Learning   Collaborative machine learning and related techniques such as federated
learning allow multiple participants, each with his own training dataset, to
build a joint model by training locally and periodically exchanging model
updates. We demonstrate that these updates leak unintended information about
participants' training data and develop passive and active inference attacks to
exploit this leakage. First, we show that an adversarial participant can infer
the presence of exact data points -- for example, specific locations -- in
others' training data (i.e., membership inference). Then, we show how this
adversary can infer properties that hold only for a subset of the training data
and are independent of the properties that the joint model aims to capture. For
example, he can infer when a specific person first appears in the photos used
to train a binary gender classifier. We evaluate our attacks on a variety of
tasks, datasets, and learning configurations, analyze their limitations, and
discuss possible defenses.

29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom