This dataset contains data from 84 participants, collected in two settings: in
the lab, and at home.
The data collected at home consists of multiple sessions performed over several
weeks. During those sessions, participants were asked to interact with their
smartphones in different body postures and movements. The dataset includes
sensor data such as accelerometer and gyroscope readings with timestamps.
The dataset provides a valuable resource for understanding the relationship
between body posture, movements, and mobile authentication performance. It can
be used by researchers to explore the impact of different body postures and
movements on mobile device security, and to develop more effective mobile
authentication methods. By sharing this dataset, we hope to contribute to the
wider research community and promote further investigation into this important
topic.
All data was collected using an iPhone XR. Each participant completed an
average of 25 sessions. During each session, subjects were asked to perform
simple tasks, such as reading, writing, and image comparison. At the end of
each reading and image comparison task, they were asked 3-5 questions about the
task. In each session, users were not required to perform the tasks in a
specific body position.
Data was collected with the approval NYIT IRB approval.
Shoulder surfing attacks are an unfortunate consequence of entering passwords
or PINs into computers, smartphones, PoS terminals, and ATMs. Such attacks
generally involve observing the victim’s input device. This project studies
leakage of user secrets (passwords and PINs) based on observations of output
devices (screens or projectors) that provide “helpful” feedback to users in the
form of masking characters, each corresponding to a keystroke. To this end, we
developed a new attack called Secret Information Leakage from Keystroke Timing
Videos (SILK-TV). Our attack extracts inter-keystroke timing information from
videos of password masking characters displayed when users type their password
on a computer, or their PIN at an ATM or PoS.
Telerobotic systems are used to perform critical tasks in sensitive
environments. The security of these systems is of paramount importance, because
compromising them can result in significant harm. This dataset represents a
first step towards addressing threats that lead to illegitimate access to
telerobotic devices. The data was collected via experiments in which users
explored several scenes using a GeoMagic Phantom Omni haptic device. These
scenes provided only limited visual feedback, and required users to interact
with it by primarily relying on haptic feedback. We recorded how 32 users
interacted with the haptic device over a total of 180 sessions.
This dataset contains data recorded using a smartphone and two smartwatches
during typing activities. Users were asked to walk down a hallway while
answering a number of questions using a custom data collection app. During each
session, a supervisor grabbed the smartphone from the hands of the subject
without prior notice. The subjects were also asked to give the smartphone to
the supervisor, and to place the smartphone on a desk in each session.