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Demo: ReCon: Revealing and Controlling PII Leaks in Mobile Network Traffic

Published: 25 June 2016 Publication History
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  • Abstract

    It is well known that apps running on mobile devices extensively track and leak users' personally identifiable information (PII) [1] and users are generally unaware and unable to stop them [2]; however, these users have little visibility into PII leaked through the network traffic generated by their devices, and have poor control over how, when and where that traffic is sent and handled by third parties.
    This demo presents the design, implementation, and evaluation of ReCon: a cross-platform (iOS, Android, Windows) system that reveals PII leaks and gives users control over them without requiring any special privileges or custom OSes. ReCon, which has been accepted as a paper to the Mobisys conference, leverages machine learning to reveal potential PII leaks by inspecting network traffic, and provides a visualization tool to empower users with the ability to control these leaks via blocking or substitution of PII. For this demo, we will show how ReCon detects PII in real time, allows users to provide feedback on detected PII, and provides users with controls for blocking or modifying PII leaks. The system is publicly available and we will also help any interested users install it on their mobile devices.

    References

    [1]
    M. C. Grace, W. Zhou, X. Jiang, and A.-R. Sadeghi. Unsafe Exposure Analysis of Mobile In-app Advertisements. In Proc. of WiSec, 2012.
    [2]
    S. Han, J. Jung, and D. Wetherall. A Study of Third-Party Tracking by Mobile Apps in the Wild. Technical Report UW-CSE-12-03-01, University of Washington, 2012.

    Cited By

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    • (2023)Mobile App Fingerprinting through Automata Learning and Machine Learning2023 IFIP Networking Conference (IFIP Networking)10.23919/IFIPNetworking57963.2023.10186420(1-9)Online publication date: 12-Jun-2023
    • (2022)Conceptual Model for Automating GDPR Compliance Verification Using Natural Language Approach2022 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)10.1109/ITSS-IoE56359.2022.9990931(1-6)Online publication date: 3-Dec-2022
    • (2022)A Survey on Mobile Malware Detection Methods using Machine Learning2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)10.1109/CCWC54503.2022.9720753(0215-0221)Online publication date: 26-Jan-2022
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    Published In

    cover image ACM Conferences
    MobiSys '16 Companion: Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services Companion
    June 2016
    172 pages
    ISBN:9781450344166
    DOI:10.1145/2938559
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 25 June 2016

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    • (2023)Mobile App Fingerprinting through Automata Learning and Machine Learning2023 IFIP Networking Conference (IFIP Networking)10.23919/IFIPNetworking57963.2023.10186420(1-9)Online publication date: 12-Jun-2023
    • (2022)Conceptual Model for Automating GDPR Compliance Verification Using Natural Language Approach2022 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)10.1109/ITSS-IoE56359.2022.9990931(1-6)Online publication date: 3-Dec-2022
    • (2022)A Survey on Mobile Malware Detection Methods using Machine Learning2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)10.1109/CCWC54503.2022.9720753(0215-0221)Online publication date: 26-Jan-2022
    • (2021)Analyzing security issues of android mobile health and medical applicationsJournal of the American Medical Informatics Association10.1093/jamia/ocab13128:10(2074-2084)Online publication date: 12-Aug-2021
    • (2018)Third Party Tracking in the Mobile EcosystemProceedings of the 10th ACM Conference on Web Science10.1145/3201064.3201089(23-31)Online publication date: 15-May-2018
    • (2018)Measuring Third-party Tracker Power across Web and MobileACM Transactions on Internet Technology10.1145/317624618:4(1-22)Online publication date: 7-Aug-2018
    • (2017)Better the Devil You KnowProceedings of the 2017 CHI Conference on Human Factors in Computing Systems10.1145/3025453.3025556(5208-5220)Online publication date: 2-May-2017

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