Abstract
The vast adoption of mobile devices in our everyday lives, apart from facilitating us through their various enhanced capabilities, has also raised serious privacy concerns. While mobile devices are equipped with numerous sensors which offer context-awareness to their installed apps, they can be also exploited to reveal sensitive information when correlated with other data or sources. Companies have introduced a plethora of privacy invasive methods to harvest user’s personal data for profiling and monetizing purposes. Nonetheless, up to now, these methods were constrained by the environment they operate, e.g. browser vs mobile app, and since only a handful of businesses could have access to both of these environments, the conceivable risks can be calculated and the involved enterprises can be somehow monitored and regulated. This work introduces some novel user deanonymisation approaches for device fingerprinting in Android. Having Android AOSP as our baseline, we prove that web pages, by using several inherent mechanisms, can cooperate with installed mobile apps to identify which sessions operate in specific devices and consequently to further expose users’ privacy.
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Acknowledgments
This work was supported by the European Commission under the Horizon 2020 Programme (H2020), as part of the OPERANDO project (Grant Agreement no. 653704) and is based upon work from COST Action CRYPTACUS, supported by COST (European Cooperation in Science and Technology). The authors would like to thank ElevenPaths for their valuable feedback and providing them access to Tacyt.
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Alepis, E., Patsakis, C. (2017). The All Seeing Eye: Web to App Intercommunication for Session Fingerprinting in Android. In: Wang, G., Atiquzzaman, M., Yan, Z., Choo, KK. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2017. Lecture Notes in Computer Science(), vol 10656. Springer, Cham. https://doi.org/10.1007/978-3-319-72389-1_9
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DOI: https://doi.org/10.1007/978-3-319-72389-1_9
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