Impostors are attackers who take over a smartphone and gain access
to the legitimate user’s confidential and private information. This
paper proposes a defense-in-depth mechanism to detect impostors
quickly with simple Deep Learning algorithms, which can achieve
better detection accuracy than the best prior work which used
Machine Learning algorithms requiring computation of multiple
features. Different from previous work, we then consider protecting
the privacy of a user’s behavioral (sensor) data by not exposing it
outside the smartphone. For this scenario, we propose a Recurrent
Neural Network (RNN) based Deep Learning algorithm that uses
only the legitimate user’s sensor data to learn his/her normal behavior.
We propose to use Prediction Error Distribution (PED) to
enhance the detection accuracy. We also show how a minimalist
hardware module, dubbed SID for Smartphone Impostor Detector,
can be designed and integrated into smartphones for self-contained
impostor detection. Experimental results show that SID can support
real-time impostor detection, at a very low hardware cost and
energy consumption, compared to other RNN accelerators.
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This content will become publicly available on November 30, 2024
Measures of Information Leakage for Incomplete Statistical Information: Application to a Binary Privacy Mechanism
- Award ID(s):
- 2030249
- NSF-PAR ID:
- 10488083
- Publisher / Repository:
- Association for Computing Machinery
- Date Published:
- Journal Name:
- ACM Transactions on Privacy and Security
- Volume:
- 26
- Issue:
- 4
- ISSN:
- 2471-2566
- Page Range / eLocation ID:
- 1 to 31
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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