research-article

Privacy Tips: Would it be ever possible to empower online social-network users to control the confidentiality of their data?

Authors:

Vladimir Estivill-Castro,

David F. NettletonAuthors Info & Claims

ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

Pages 1449 - 1456

https://doi.org/10.1145/2808797.2809279

Published: 25 August 2015 Publication History

Abstract

Using the web for communication, purchases, searching information and/or socializing generates data, about ourselves, our connections and our activities, which is collected easily. In online social networks, users volunteer perhaps what is considered more personal information to their selected circles. But each person has personal preferences about what it considers public and what it considers private. The problem is that the information that is public may be used to disclose information that the users expect to remain confidential.

This paper offers a path to provide tips and warnings to each user of an online social network so they can exercise control on the information they consider private not only by not disclosing such information, but by acting on their public information-items that could be informative for those information-items that are private. This is a significant challenge, because most web-applications use personalization to build a context and provide better services. We aim to raise awareness on privacy and to empower users, giving them the possibility to regulate the benefits of personalization with the privacy risks. In this paper we also show that information-items (like relationships) can be chosen as confidential, and that we can provide meaningful warnings on metrics of association and public attributes that are strong predictors of confidential information-items.

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Cited By

Reza KIslam MEstivill-Castro V(2021)Privacy protection of online social network users, against attribute inference attacks, through the use of a set of exhaustive rulesNeural Computing and Applications10.1007/s00521-021-05860-8Online publication date: 8-Apr-2021
https://doi.org/10.1007/s00521-021-05860-8
Reza KIslam MEstivill-Castro V(2020)Protection of User-Defined Sensitive Attributes on Online Social Networks Against Attribute Inference Attack via Adversarial Data MiningInformation Systems Security and Privacy10.1007/978-3-030-49443-8_11(230-249)Online publication date: 28-Jun-2020
https://doi.org/10.1007/978-3-030-49443-8_11
Reza KIslam MEstivill-Castro V(2017)Social media users' privacy against malicious data miners2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)10.1109/ISKE.2017.8258834(1-8)Online publication date: Nov-2017
https://doi.org/10.1109/ISKE.2017.8258834
Show More Cited By

Privacy Tips: Would it be ever possible to empower online social-network users to control the confidentiality of their data?
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
2. Information systems
  1. Information systems applications

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cover image ACM Conferences

ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

August 2015

835 pages

ISBN:9781450338547

DOI:10.1145/2808797

Copyright © 2015 ACM.

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: 25 August 2015

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Ministerio de Economía y Competitividad, Spain

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ASONAM '15

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SIGKDD

ASONAM '15: Advances in Social Networks Analysis and Mining 2015

August 25 - 28, 2015

Paris, France

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Reza KIslam MEstivill-Castro V(2021)Privacy protection of online social network users, against attribute inference attacks, through the use of a set of exhaustive rulesNeural Computing and Applications10.1007/s00521-021-05860-8Online publication date: 8-Apr-2021
https://doi.org/10.1007/s00521-021-05860-8
Reza KIslam MEstivill-Castro V(2020)Protection of User-Defined Sensitive Attributes on Online Social Networks Against Attribute Inference Attack via Adversarial Data MiningInformation Systems Security and Privacy10.1007/978-3-030-49443-8_11(230-249)Online publication date: 28-Jun-2020
https://doi.org/10.1007/978-3-030-49443-8_11
Reza KIslam MEstivill-Castro V(2017)Social media users' privacy against malicious data miners2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)10.1109/ISKE.2017.8258834(1-8)Online publication date: Nov-2017
https://doi.org/10.1109/ISKE.2017.8258834
Torra VTorra V(2017)Privacy Models and Disclosure Risk MeasuresData Privacy: Foundations, New Developments and the Big Data Challenge10.1007/978-3-319-57358-8_5(111-189)Online publication date: 18-May-2017
https://doi.org/10.1007/978-3-319-57358-8_5

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