2012
DOI: 10.1142/s0218488512400284
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A Metric to Evaluate Interaction Obfuscation in Online Social Networks

Abstract: Online social networks (OSNs) have become one of the main communication channels in today's information society, and their emergence has raised new privacy concerns. The content uploaded to OSNs (such as pictures, status updates, comments) is by default available to the OSN provider, and often to other people to whom the user who uploaded the content did not intend to give access. A different class of concerns relates to sensitive information that can be inferred from the behavior of users. For example, the an… Show more

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Cited by 9 publications
(9 citation statements)
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“…Analogously to the case of OSN communication modelling, the absence of correlations allows a designer to treat these features independently Balsa et al (2012). Effective obfuscation requires plausibility.…”
Section: Discussionmentioning
confidence: 99%
See 3 more Smart Citations
“…Analogously to the case of OSN communication modelling, the absence of correlations allows a designer to treat these features independently Balsa et al (2012). Effective obfuscation requires plausibility.…”
Section: Discussionmentioning
confidence: 99%
“…Our work is further inspired by the design of obfuscation tools for traffic analysis resistance in online social networks Balsa et al (2012). The goal of these tools is to prevent an adversary (be it the service provider or an external adversary) from profiling the users' communication patterns, namely, to accurately determine with whom and how often OSN users communicate.…”
Section: Obfuscation Tools For Traffic Analysis Resistancementioning
confidence: 99%
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“…In fact, they aim to hide the content from un authorized viewers, with main focus on the OSN provider. Nonetheless, as observed in [3] the OSN provider is still able to learns and to extract extra sensitive information based on users behavior, such as the strength of relationships, by employing data mining techniques. Hence, in this paper, we address a specific class of privacy concerns, i.e., where sensitive information can be inferred from the behavior and connections of users.…”
Section: Introductionmentioning
confidence: 99%