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Short paper: location privacy: user behavior in the field

Published: 19 October 2012 Publication History

Abstract

Current smartphone platforms provide ways for users to control access to information about their location. For instance, on the iPhone, when an application requests access to location information, the operating system asks the user whether to grant location access to this application. In this paper, we study how users are using these controls. Do iPhone users allow applications to access their location? Do their decisions differ from application to application? Can we predict how a user will respond for a particular application, given their past responses for other applications?
We gather data from iPhone users that sheds new light on these questions. Our results indicate that there are different classes of users: some deny all applications access to their location, some allow all applications access to their location, and some selectively permit a fraction of their applications to access their location. We also find that apps can be separated into different classes by what fraction of users trust the app with their location data. Finally, we investigate using machine learning techniques to predict users' location-sharing decisions; we find that we are sometimes able to predict the user's actual choice, though there is considerable room for improvement. If it is possible to improve the accuracy rate further, this information could be used to relieve users of the cognitive burden of individually assigning location permissions for each application, allowing users to focus their attention on more critical matters.

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Cited By

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  • (2024)Understanding Users' Perspectives on Location Privacy Management on iPhonesProceedings of the ACM on Human-Computer Interaction10.1145/36765298:MHCI(1-25)Online publication date: 24-Sep-2024
  • (2023)“Hello, Fellow Villager!”: Perceptions and Impact of Displaying Users’ Locations on WeiboHuman-Computer Interaction – INTERACT 202310.1007/978-3-031-42286-7_29(511-532)Online publication date: 25-Aug-2023
  • (2021)Exploring Perceptions of a Localized Content-Sharing System Using Users-as-BeaconsHuman-Computer Interaction – INTERACT 202110.1007/978-3-030-85616-8_21(351-372)Online publication date: 26-Aug-2021
  • Show More Cited By

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    Published In

    cover image ACM Conferences
    SPSM '12: Proceedings of the second ACM workshop on Security and privacy in smartphones and mobile devices
    October 2012
    112 pages
    ISBN:9781450316668
    DOI:10.1145/2381934
    • General Chair:
    • Ting Yu,
    • Program Chairs:
    • William Enck,
    • Xuxian Jiang
    Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 19 October 2012

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    Author Tags

    1. privacy
    2. smartphones

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    CCS'12
    Sponsor:
    CCS'12: the ACM Conference on Computer and Communications Security
    October 19, 2012
    North Carolina, Raleigh, USA

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    Overall Acceptance Rate 46 of 139 submissions, 33%

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    Cited By

    View all
    • (2024)Understanding Users' Perspectives on Location Privacy Management on iPhonesProceedings of the ACM on Human-Computer Interaction10.1145/36765298:MHCI(1-25)Online publication date: 24-Sep-2024
    • (2023)“Hello, Fellow Villager!”: Perceptions and Impact of Displaying Users’ Locations on WeiboHuman-Computer Interaction – INTERACT 202310.1007/978-3-031-42286-7_29(511-532)Online publication date: 25-Aug-2023
    • (2021)Exploring Perceptions of a Localized Content-Sharing System Using Users-as-BeaconsHuman-Computer Interaction – INTERACT 202110.1007/978-3-030-85616-8_21(351-372)Online publication date: 26-Aug-2021
    • (2020)Image Privacy Prediction Using Deep Neural NetworksACM Transactions on the Web10.1145/338608214:2(1-32)Online publication date: 9-Apr-2020
    • (2019)Privacy-Enhancing Context Authentication from Location-Sensitive DataProceedings of the 14th International Conference on Availability, Reliability and Security10.1145/3339252.3340334(1-10)Online publication date: 26-Aug-2019
    • (2019)Catering to Your ConcernsACM Transactions on Cyber-Physical Systems10.1145/33176993:4(1-21)Online publication date: 4-Sep-2019
    • (2019)The impact of transparency on mobile privacy decision makingElectronic Markets10.1007/s12525-019-00332-330:3(607-625)Online publication date: 7-Feb-2019
    • (2018)FoxITProceedings of the 7th Workshop on Socio-Technical Aspects in Security and Trust10.1145/3167996.3167999(53-63)Online publication date: 5-Dec-2018
    • (2017)Does this App Really Need My Location?Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/31320291:3(1-22)Online publication date: 11-Sep-2017
    • (2016)Follow my recommendationsProceedings of the Twelfth USENIX Conference on Usable Privacy and Security10.5555/3235895.3235899(27-41)Online publication date: 22-Jun-2016
    • Show More Cited By

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