7,061 research outputs found
EyeSpot: leveraging gaze to protect private text content on mobile devices from shoulder surfing
As mobile devices allow access to an increasing amount of private data, using them in public can potentially leak sensitive information through shoulder surfing. This includes personal private data (e.g., in chat conversations) and business-related content (e.g., in emails). Leaking the former might infringe on users’ privacy, while leaking the latter is considered a breach of the EU’s General Data Protection Regulation as of May 2018. This creates a need for systems that protect sensitive data in public. We introduce EyeSpot, a technique that displays content through a spot that follows the user’s gaze while hiding the rest of the screen from an observer’s view through overlaid masks. We explore different configurations for EyeSpot in a user study in terms of users’ reading speed, text comprehension, and perceived workload. While our system is a proof of concept, we identify crystallized masks as a promising design candidate for further evaluation with regard to the security of the system in a shoulder surfing scenario
POWER-SUPPLaY: Leaking Data from Air-Gapped Systems by Turning the Power-Supplies Into Speakers
It is known that attackers can exfiltrate data from air-gapped computers
through their speakers via sonic and ultrasonic waves. To eliminate the threat
of such acoustic covert channels in sensitive systems, audio hardware can be
disabled and the use of loudspeakers can be strictly forbidden. Such audio-less
systems are considered to be \textit{audio-gapped}, and hence immune to
acoustic covert channels.
In this paper, we introduce a technique that enable attackers leak data
acoustically from air-gapped and audio-gapped systems. Our developed malware
can exploit the computer power supply unit (PSU) to play sounds and use it as
an out-of-band, secondary speaker with limited capabilities. The malicious code
manipulates the internal \textit{switching frequency} of the power supply and
hence controls the sound waveforms generated from its capacitors and
transformers. Our technique enables producing audio tones in a frequency band
of 0-24khz and playing audio streams (e.g., WAV) from a computer power supply
without the need for audio hardware or speakers. Binary data (files,
keylogging, encryption keys, etc.) can be modulated over the acoustic signals
and sent to a nearby receiver (e.g., smartphone). We show that our technique
works with various types of systems: PC workstations and servers, as well as
embedded systems and IoT devices that have no audio hardware at all. We provide
technical background and discuss implementation details such as signal
generation and data modulation. We show that the POWER-SUPPLaY code can operate
from an ordinary user-mode process and doesn't need any hardware access or
special privileges. Our evaluation shows that using POWER-SUPPLaY, sensitive
data can be exfiltrated from air-gapped and audio-gapped systems from a
distance of five meters away at a maximal bit rates of 50 bit/sec
ABAKA : a novel attribute-based k-anonymous collaborative solution for LBSs
The increasing use of mobile devices, along with advances in telecommunication systems, increased the popularity of Location-Based Services (LBSs). In LBSs, users share their exact location with a potentially untrusted Location-Based Service Provider (LBSP). In such a scenario, user privacy becomes a major con- cern: the knowledge about user location may lead to her identification as well as a continuous tracing of her position. Researchers proposed several approaches to preserve users’ location privacy. They also showed that hiding the location of an LBS user is not enough to guarantee her privacy, i.e., user’s pro- file attributes or background knowledge of an attacker may reveal the user’s identity. In this paper we propose ABAKA, a novel collaborative approach that provides identity privacy for LBS users considering users’ profile attributes. In particular, our solution guarantees p -sensitive k -anonymity for the user that sends an LBS request to the LBSP. ABAKA computes a cloaked area by collaborative multi-hop forwarding of the LBS query, and using Ciphertext-Policy Attribute-Based Encryption (CP-ABE). We ran a thorough set of experiments to evaluate our solution: the results confirm the feasibility and efficiency of our proposal
Understanding Shoulder Surfing in the Wild: Stories from Users and Observers
Research has brought forth a variety of authentication systems to mitigate observation attacks. However, there is little work about shoulder surfing situations in the real world. We present the results of a user survey (N=174) in which we investigate actual stories about shoulder surfing on mobile devices from both users and observers. Our analysis indicates that shoulder surfing mainly occurs in an opportunistic, non-malicious way. It usually does not have serious consequences, but evokes negative feelings for both parties, resulting in a variety of coping strategies. Observed data was personal in most cases and ranged from information about interests and hobbies to login data and intimate details about third persons and relationships. Thus, our work contributes evidence for shoulder surfing in the real world and informs implications for the design of privacy protection mechanisms
Implicit Sensor-based Authentication of Smartphone Users with Smartwatch
Smartphones are now frequently used by end-users as the portals to
cloud-based services, and smartphones are easily stolen or co-opted by an
attacker. Beyond the initial log-in mechanism, it is highly desirable to
re-authenticate end-users who are continuing to access security-critical
services and data, whether in the cloud or in the smartphone. But attackers who
have gained access to a logged-in smartphone have no incentive to
re-authenticate, so this must be done in an automatic, non-bypassable way.
Hence, this paper proposes a novel authentication system, iAuth, for implicit,
continuous authentication of the end-user based on his or her behavioral
characteristics, by leveraging the sensors already ubiquitously built into
smartphones. We design a system that gives accurate authentication using
machine learning and sensor data from multiple mobile devices. Our system can
achieve 92.1% authentication accuracy with negligible system overhead and less
than 2% battery consumption.Comment: Published in Hardware and Architectural Support for Security and
Privacy (HASP), 201
- …