New Approach to Quantification of Privacy on Social Network Sites

Users may unintentionally reveal private information to the world on their blogs on social network sites (SNSs). Information hunters can exploit such disclosed sensitive information for the purpose of advertising, marketing, spamming, etc. We present a new metric to quantify privacy, based on probab...

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Bibliographic Details
Published in2010 24th IEEE International Conference on Advanced Information Networking and Applications pp. 556 - 564
Main Authors Tran Hong Ngoc, Echizen, Isao, Komei, Kamiyama, Yoshiura, Hiroshi
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2010
Subjects
Blogs
Data privacy
Entropy
Joints
Measurement
Privacy
privacy metric
privacy on SNS
privacy quantification
Probability distribution
SNS
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Summary:Users may unintentionally reveal private information to the world on their blogs on social network sites (SNSs). Information hunters can exploit such disclosed sensitive information for the purpose of advertising, marketing, spamming, etc. We present a new metric to quantify privacy, based on probability and entropy theory. Simply by relying on the total leaked privacy value calculated with our metric, users can adjust the amount of information they reveal on SNSs. Previous studies focused on quantifying privacy for purposes of data mining and location finding. The privacy metric in this paper deals with unintentional leaks of information from SNSs. Our metric helps users of SNSs find how much privacy can be preserved after they have published sentences on their SNSs. It is simple, yet precise, which is proved through an experimental evaluation.
ISBN:9781424466955
1424466954
ISSN:1550-445X
2332-5658
DOI:10.1109/AINA.2010.118