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Privacy-Preserving Enhanced Collaborative Tagging

delete2014-01-01
delete28
PRE
AI
J
Javier Parra‐Arnau *
A
Andrea Perego
E
Elena Ferrari
J
Jordi Forné
D
David Rebollo‐Monedero
DOI:10.1109/TKDE.2012.248delete
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摘要

摘要

En 中文
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.
Keyword:
Policy-based collaborative tagging
social bookmarking
tag suppression
privacy-enhancing technology
Shannon's entropy
privacy-utility tradeoff
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期刊

IEEE Transactions on Knowledge and Data Engineering 封面图
IEEE Transactions on Knowledge and Data Engineering
IF:
10.4
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6.9K
被引数:
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ec jrc ispra site
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European Commission Joint Research Centre
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universitat politecnica de catalunya
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