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Analyzing facebook privacy settings: user expectations vs. reality

Published: 02 November 2011 Publication History

Abstract

The sharing of personal data has emerged as a popular activity over online social networking sites like Facebook. As a result, the issue of online social network privacy has received significant attention in both the research literature and the mainstream media. Our overarching goal is to improve defaults and provide better tools for managing privacy, but we are limited by the fact that the full extent of the privacy problem remains unknown; there is little quantification of the incidence of incorrect privacy settings or the difficulty users face when managing their privacy.
In this paper, we focus on measuring the disparity between the desired and actual privacy settings, quantifying the magnitude of the problem of managing privacy. We deploy a survey, implemented as a Facebook application, to 200 Facebook users recruited via Amazon Mechanical Turk. We find that 36% of content remains shared with the default privacy settings. We also find that, overall, privacy settings match users' expectations only 37% of the time, and when incorrect, almost always expose content to more users than expected. Finally, we explore how our results have potential to assist users in selecting appropriate privacy settings by examining the user-created friend lists. We find that these have significant correlation with the social network, suggesting that information from the social network may be helpful in implementing new tools for managing privacy.

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        cover image ACM Conferences
        IMC '11: Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
        November 2011
        612 pages
        ISBN:9781450310130
        DOI:10.1145/2068816
        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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        Published: 02 November 2011

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        Author Tags

        1. facebook
        2. measurement
        3. online social networks
        4. privacy

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        IMC '11
        IMC '11: Internet Measurement Conference
        November 2 - 4, 2011
        Berlin, Germany

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        • (2024)Demystifying the Privacy-Realism Dilemma in the MetaverseProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering Workshops10.1145/3691621.3694958(245-250)Online publication date: 27-Oct-2024
        • (2024)NewsGuesser: Using Curiosity to Reduce Selective ExposureProceedings of the ACM on Human-Computer Interaction10.1145/36373768:CSCW1(1-22)Online publication date: 26-Apr-2024
        • (2024)What is my privacy score? Measuring users’ privacy on social networking websitesElectronic Commerce Research10.1007/s10660-023-09796-0Online publication date: 1-Feb-2024
        • (2024)Privacy Perceptions and Behaviors of Google Personal Account Holders in Saudi ArabiaHCI for Cybersecurity, Privacy and Trust10.1007/978-3-031-61379-1_1(3-29)Online publication date: 1-Jun-2024
        • (2023)User awareness and behaviors concerning encrypted DNS settings in web browsersProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620412(3117-3133)Online publication date: 9-Aug-2023
        • (2023)Designing Alternative Form-Autocompletion Tools to Enhance Privacy Decision-making and Prevent Unintended DisclosureACM Transactions on Computer-Human Interaction10.1145/361036630:6(1-42)Online publication date: 25-Sep-2023
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        • (2023)Twitter has a Binary Privacy Setting, are Users Aware of How It Works?Proceedings of the ACM on Human-Computer Interaction10.1145/35795377:CSCW1(1-18)Online publication date: 16-Apr-2023
        • (2023)Less is Not More: Improving Findability and Actionability of Privacy Controls for Online Behavioral AdvertisingProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580773(1-33)Online publication date: 19-Apr-2023
        • (2023)Rethinking Creative Labor: A Sociotechnical Examination of Creativity & Creative Work on TikTokProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580649(1-16)Online publication date: 19-Apr-2023
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