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"I knew they clicked when i saw them with their friends": identifying your silent web visitors on social media

Published: 01 October 2014 Publication History

Abstract

An increasing fraction of users access content on the web from social media. Endorsements by microbloggers and public figures you connect with gradually replaces the curation originally in the hand of traditional media sources. One expects a social media provider to possess a unique ability to analyze audience and trends since they collect not only information about what you actively share, but also about what you silently watch. Your behavior in the latter seems safe from observations outside your online service provider, for privacy but also commercial reasons.
In this paper, we show that supposing that your passive web visits are anonymous to your host is a fragile assumption, or alternatively that third parties -- content publishers or providers serving ads onto them -- can efficiently reconciliate visitors with their social media identities. What is remarkable in this technique is that it need no support from the social media provider, it seamlessly applies to visitors who \emph{never} post or endorse content, and a visitor's public identity become known after a few clicks. This method combines properties of the public follower graph with posting behaviors and recent time-based inference, making it difficult to evade without drastic or time-wasting measures. It potentially offers researchers working on traffic datasets a new view into who access content or through which channels.

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    cover image ACM Conferences
    COSN '14: Proceedings of the second ACM conference on Online social networks
    October 2014
    288 pages
    ISBN:9781450331982
    DOI:10.1145/2660460
    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: 01 October 2014

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    Author Tags

    1. data mining
    2. privacy
    3. social networks

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    COSN'14
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    COSN'14: Conference on Online Social Networks
    October 1 - 2, 2014
    Dublin, Ireland

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    COSN '14 Paper Acceptance Rate 25 of 87 submissions, 29%;
    Overall Acceptance Rate 69 of 307 submissions, 22%

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    View all
    • (2024)Efficiency Boosts in Human Mobility Data Privacy Risk Assessment: Advancements within the PRUDEnce FrameworkApplied Sciences10.3390/app1417801414:17(8014)Online publication date: 7-Sep-2024
    • (2024)TrajectGuard: A Comprehensive Privacy-Risk Framework for Multiple-Aspects TrajectoriesIEEE Access10.1109/ACCESS.2024.346208812(136354-136378)Online publication date: 2024
    • (2018)Resource Dependency Processing in Web Scaling FrameworksIEEE Transactions on Services Computing10.1109/TSC.2016.256193411:1(155-168)Online publication date: 1-Jan-2018
    • (2018)On the privacy-conscientious use of mobile phone dataScientific Data10.1038/sdata.2018.2865:1Online publication date: 11-Dec-2018
    • (2017)A Data Mining Approach to Assess Privacy Risk in Human Mobility DataACM Transactions on Intelligent Systems and Technology10.1145/31067749:3(1-27)Online publication date: 11-Dec-2017
    • (2017)De-anonymizing Web Browsing Data with Social NetworksProceedings of the 26th International Conference on World Wide Web10.1145/3038912.3052714(1261-1269)Online publication date: 3-Apr-2017

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