Proceedings of the Third ACM Conference on Data and Application Security and Privacy 2013
DOI: 10.1145/2435349.2435352
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Do online social network friends still threaten my privacy?

Abstract: A user's online social network (OSN) friends commonly share information on their OSN profiles that might also characterize the user him-/herself. Therefore, OSN friends are potentially jeopardizing users' privacy. Previous studies demonstrated that third parties can potentially infer personally identifiable information (PII) based on information shared by users' OSN friends if sufficient information is accessible. However, when considering how privacy settings have been adjusted since then, it is unclear which… Show more

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Cited by 15 publications
(13 citation statements)
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“…Fang et al [185], Labitzke et al [186], Youyang et al [187] 7. spatial and temporal information of the SN use fusing temporal and spatial data with other related information such as check-in/posts data to infer private information of users.…”
Section: B De-anonymization Methods Employed By the Adversaries To Jmentioning
confidence: 99%
“…Fang et al [185], Labitzke et al [186], Youyang et al [187] 7. spatial and temporal information of the SN use fusing temporal and spatial data with other related information such as check-in/posts data to infer private information of users.…”
Section: B De-anonymization Methods Employed By the Adversaries To Jmentioning
confidence: 99%
“…7 We have put distances into 100 mile buckets. 8 Locale is the user chosen interface language for Facebook.com, such as EN US (English in USA), EN GB (English in Great Britain) In the previous sections, we have shown how different dimensions of user generated texts affect how many times a particular tweet/comment will be liked or commented. Regardless of these numbers, our geolocation experiments show that users who like/comment a text are more likely to live closer to the owner of the text.…”
Section: Influence Of Geolocation On Conversationsmentioning
confidence: 98%
“…The study [53] inferred user's attributes by emotionoriented mining and predicted user's attributes by construct an association graph based on public friends. F A Zamal et al evaluated the inference accuracy obtained by using features in Twitter profile and friend posts to enhance user attributes, showed that different subsamples of user neighbourhood depict different aspects of users themselves [54].…”
Section: E Othersmentioning
confidence: 99%