Unauthorized Location Inference Using Smart Device Pressure Sensor

Ahmed Ibrahim, Ahmed Eltawil

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Scopus citations


    Accessing private user information through smart devices has become an increasing concern recently. Even though smart device operating systems require user permission for perceived sensitive information, side channel attacks using inference can result in serious security breaches. In this work, we present an online learning method that infers location from raw pressure sensor data. The online method mitigates the error arising from changing weather conditions, without the need to obtain any data from weather stations. We demonstrate how a long short-term memory (LSTM) neural network trained by sequences of locations and their corresponding pressure patterns is capable of inferring user location. Experimental results showing the accuracy of the proposed system are presented.
    Original languageEnglish (US)
    Title of host publication2020 9th Mediterranean Conference on Embedded Computing (MECO)
    PublisherInstitute of Electrical and Electronics Engineers (IEEE)
    ISBN (Print)9781728169491
    StatePublished - Jun 2020

    Bibliographical note

    KAUST Repository Item: Exported on 2021-04-13
    Acknowledgements: We would like to express our deep gratitude towards UCI Anteater Express shuttle service management for their help in collecting the data needed to validate the proposed methodology.


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