2,676 research outputs found
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
Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning
Authentication of smartphone users is important because a lot of sensitive
data is stored in the smartphone and the smartphone is also used to access
various cloud data and services. However, smartphones are easily stolen or
co-opted by an attacker. Beyond the initial login, it is highly desirable to
re-authenticate end-users who are continuing to access security-critical
services and data. Hence, this paper proposes a novel authentication system for
implicit, continuous authentication of the smartphone user based on behavioral
characteristics, by leveraging the sensors already ubiquitously built into
smartphones. We propose novel context-based authentication models to
differentiate the legitimate smartphone owner versus other users. We
systematically show how to achieve high authentication accuracy with different
design alternatives in sensor and feature selection, machine learning
techniques, context detection and multiple devices. Our system can achieve
excellent authentication performance with 98.1% accuracy with negligible system
overhead and less than 2.4% battery consumption.Comment: Published on the IEEE/IFIP International Conference on Dependable
Systems and Networks (DSN) 2017. arXiv admin note: substantial text overlap
with arXiv:1703.0352
Quantification of De-anonymization Risks in Social Networks
The risks of publishing privacy-sensitive data have received considerable
attention recently. Several de-anonymization attacks have been proposed to
re-identify individuals even if data anonymization techniques were applied.
However, there is no theoretical quantification for relating the data utility
that is preserved by the anonymization techniques and the data vulnerability
against de-anonymization attacks.
In this paper, we theoretically analyze the de-anonymization attacks and
provide conditions on the utility of the anonymized data (denoted by anonymized
utility) to achieve successful de-anonymization. To the best of our knowledge,
this is the first work on quantifying the relationships between anonymized
utility and de-anonymization capability. Unlike previous work, our
quantification analysis requires no assumptions about the graph model, thus
providing a general theoretical guide for developing practical
de-anonymization/anonymization techniques.
Furthermore, we evaluate state-of-the-art de-anonymization attacks on a
real-world Facebook dataset to show the limitations of previous work. By
comparing these experimental results and the theoretically achievable
de-anonymization capability derived in our analysis, we further demonstrate the
ineffectiveness of previous de-anonymization attacks and the potential of more
powerful de-anonymization attacks in the future.Comment: Published in International Conference on Information Systems Security
and Privacy, 201
Secure Pick Up: Implicit Authentication When You Start Using the Smartphone
We propose Secure Pick Up (SPU), a convenient, lightweight, in-device,
non-intrusive and automatic-learning system for smartphone user authentication.
Operating in the background, our system implicitly observes users' phone
pick-up movements, the way they bend their arms when they pick up a smartphone
to interact with the device, to authenticate the users.
Our SPU outperforms the state-of-the-art implicit authentication mechanisms
in three main aspects: 1) SPU automatically learns the user's behavioral
pattern without requiring a large amount of training data (especially those of
other users) as previous methods did, making it more deployable. Towards this
end, we propose a weighted multi-dimensional Dynamic Time Warping (DTW)
algorithm to effectively quantify similarities between users' pick-up
movements; 2) SPU does not rely on a remote server for providing further
computational power, making SPU efficient and usable even without network
access; and 3) our system can adaptively update a user's authentication model
to accommodate user's behavioral drift over time with negligible overhead.
Through extensive experiments on real world datasets, we demonstrate that SPU
can achieve authentication accuracy up to 96.3% with a very low latency of 2.4
milliseconds. It reduces the number of times a user has to do explicit
authentication by 32.9%, while effectively defending against various attacks.Comment: Published on ACM Symposium on Access Control Models and Technologies
(SACMAT) 201
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