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Reconstruction Attacks Against Mobile-Based Continuous Authentication Systems in the Cloud
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DOI：10.1109/TIFS.2016.2594132.png)
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Continuous authentication for mobile devices using behavioral biometrics is being suggested to complement initial authentication for securing mobile devices, and the cloud services accessed through them. This area has been studied over the past few years, and low error rates were achieved; however, it was based on training and testing using support vector machine (SVM) and other non-privacy-preserving machine learning algorithms. To stress the importance of carefully designed privacy-preserving systems, we investigate the possibility of reconstructing gestures raw data from users' authentication profiles or synthesized samples' testing results. We propose two types of reconstruction attacks based on whether actual user samples are available to the adversary (as in SVM profiles) or not. We also propose two algorithms to reconstruct raw data: a numerical-based algorithm that is specific to one compromised system, and a randomization-based algorithm that can work against almost any compromised system. For our experiments, we selected one compromised and four attacked gesture-based continuous authentication systems from the recent literature. The experiments, performed using a public data set, showed that the attacks were feasible, with a median ranging from 80% to 100% against one attacked system using all types of attacks and algorithms, and a median ranging from 73% to 100% against all attacked systems using the randomization-based algorithm and the negative support vector attack. Finally, we analyze the results, and provide recommendations for building active authentication systems that could resist reconstruction attacks.
Keyword:
Mobile devices
continuous authentication
gestures
privacy
reconstruction attacks
machine learning
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