Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Privacy-Enhancing Fall Detection from Remote Sensor Data Using
  Multi-Party Computation   Motion-based fall detection systems are concerned with detecting falls from
vulnerable users, which is typically performed by classifying measurements from
a body-worn inertial measurement unit (IMU) using machine learning. Such
systems, however, necessitate the collection of high-resolution measurements
that may violate users' privacy, such as revealing their gait, activities of
daily living (ADLs), and relative position using dead reckoning. In this paper,
we investigate the application of multi-party computation (MPC) to IMU-based
fall detection for protecting device measurement confidentiality. Our system is
evaluated in a cloud-based setting that precludes parties from learning the
underlying data using multiple, disparate cloud instances deployed in three
geographical configurations. Using a publicly-available dataset, we demonstrate
that MPC-based fall detection from IMU measurements is practical while
achieving state-of-the-art error rates. In the best case, our system executes
in 365.2 milliseconds, which falls well within the required time window for
on-device data acquisition (750ms).

21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin