Quantifying privacy violations
SDM'11 Proceedings of the 8th VLDB international conference on Secure data management
Maximizing circle of trust in online social networks
Proceedings of the 23rd ACM conference on Hypertext and social media
The walls have ears: optimize sharing for visibility and privacy in online social networks
Proceedings of the 21st ACM international conference on Information and knowledge management
A time-evolution model for the privacy degree of information disseminated in online social networks
International Journal of Communication Networks and Distributed Systems
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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.