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Exploiting vulnerability to secure user privacy on a social networking site

Published: 21 August 2011 Publication History

Abstract

As (one's) social network expands, a user's privacy protection goes beyond his privacy settings and becomes a social networking problem. In this research, we aim to address some critical issues related to privacy protection: Would the highest privacy settings guarantee a secure protection? Given the open nature of social networking sites, is it possible to manage one's privacy protection? With the diversity of one's social media friends, how can one figure out an effective approach to balance between vulnerability and privacy? We present a novel way to define a vulnerable friend from an individual user's perspective is dependent on whether or not the user's friends' privacy settings protect the friend and the individual's network of friends (which includes the user). As a single vulnerable friend in a user's social network might place all friends at risk, we resort to experiments and observe how much security an individual user can improve by unfriending a vulnerable friend. We also show how privacy weakens if newly accepted friends are unguarded or unprotected. This work provides a large-scale evaluation of new security and privacy indexes using a Facebook dataset. We present and discuss a new perspective for reasoning about social networking security. When a user accepts a new friend, the user should ensure that the new friend is not an increased security risk with the potential of negatively impacting the entire friend network. Additionally, by leveraging the indexes proposed and employing new strategies for unfriending vulnerable friends, it is possible to further improve security and privacy without changing the social networking site's existing architecture.

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  • (2023)Bottom-up psychosocial interventions for interdependent privacy: Effectiveness based on individual and content differencesProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581117(1-20)Online publication date: 19-Apr-2023
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  • (2021)Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive SurveyIEEE Access10.1109/ACCESS.2020.30457009(8512-8545)Online publication date: 2021
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cover image ACM Conferences
KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
August 2011
1446 pages
ISBN:9781450308137
DOI:10.1145/2020408
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 August 2011

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Author Tags

  1. privacy
  2. social network
  3. vulnerability

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KDD '11
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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

View all
  • (2023)Bottom-up psychosocial interventions for interdependent privacy: Effectiveness based on individual and content differencesProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581117(1-20)Online publication date: 19-Apr-2023
  • (2021)Towards an Embedding-Based Approach for the Geolocation of Texts and Users on Social NetworksInterdisciplinary Approaches to Spatial Optimization Issues10.4018/978-1-7998-1954-7.ch012(206-234)Online publication date: 2021
  • (2021)Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive SurveyIEEE Access10.1109/ACCESS.2020.30457009(8512-8545)Online publication date: 2021
  • (2021)A machine learning based approach for user privacy preservation in social networksPeer-to-Peer Networking and Applications10.1007/s12083-020-01068-014:3(1596-1607)Online publication date: 9-Mar-2021
  • (2020)Location inference for hidden population with online text analysisInternational Journal of Health Geographics10.1186/s12942-020-00245-x19:1Online publication date: 9-Dec-2020
  • (2019)Unprotected Data: Review of Internet Enabled Psychological and Information WarfareLand Forces Academy Review10.2478/raft-2019-002224:3(187-198)Online publication date: 23-Oct-2019
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