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Privacy-Preserving Enhanced Collaborative Tagging.

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Title: Privacy-Preserving Enhanced Collaborative Tagging.
Authors: Parra-Arnau, Javier1, Perego, Andrea2, Ferrari, Elena3, Forne, Jordi1, Rebollo-Monedero, David1
Source: IEEE Transactions on Knowledge & Data Engineering. Jan2014, Vol. 26 Issue 1, p180-193. 14p.
Subjects: Tags (Metadata), User-generated content, Data security, Data corruption, Database security, Social bookmarks, Uncertainty (Information theory)
Abstract: Collaborative tagging is one of the most popular services available online, and it allows end user to loosely classify either online or offline resources based on their feedback, expressed in the form of free-text labels (i.e., tags). Although tags may not be per se sensitive information, the wide use of collaborative tagging services increases the risk of cross referencing, thereby seriously compromising user privacy. In this paper, we make a first contribution toward the development of a privacy-preserving collaborative tagging service, by showing how a specific privacy-enhancing technology, namely tag suppression, can be used to protect end-user privacy. Moreover, we analyze how our approach can affect the effectiveness of a policy-based collaborative tagging system that supports enhanced web access functionalities, like content filtering and discovery, based on preferences specified by end users. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Knowledge & Data Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="DE" term="%22Tags+%28Metadata%29%22">Tags (Metadata)</searchLink><br /><searchLink fieldCode="DE" term="%22User-generated+content%22">User-generated content</searchLink><br /><searchLink fieldCode="DE" term="%22Data+security%22">Data security</searchLink><br /><searchLink fieldCode="DE" term="%22Data+corruption%22">Data corruption</searchLink><br /><searchLink fieldCode="DE" term="%22Database+security%22">Database security</searchLink><br /><searchLink fieldCode="DE" term="%22Social+bookmarks%22">Social bookmarks</searchLink><br /><searchLink fieldCode="DE" term="%22Uncertainty+%28Information+theory%29%22">Uncertainty (Information theory)</searchLink>
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  Data: Collaborative tagging is one of the most popular services available online, and it allows end user to loosely classify either online or offline resources based on their feedback, expressed in the form of free-text labels (i.e., tags). Although tags may not be per se sensitive information, the wide use of collaborative tagging services increases the risk of cross referencing, thereby seriously compromising user privacy. In this paper, we make a first contribution toward the development of a privacy-preserving collaborative tagging service, by showing how a specific privacy-enhancing technology, namely tag suppression, can be used to protect end-user privacy. Moreover, we analyze how our approach can affect the effectiveness of a policy-based collaborative tagging system that supports enhanced web access functionalities, like content filtering and discovery, based on preferences specified by end users. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IEEE Transactions on Knowledge & Data Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/TKDE.2012.248
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        Text: English
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        Type: general
      – SubjectFull: User-generated content
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      – SubjectFull: Data security
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      – SubjectFull: Social bookmarks
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              Text: Jan2014
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