Analysis of privacy in online social networks from the graph theory perspective

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Since this data can be used to infer information about the daily activities of energy consumers, it is important that utility companies and their consumers employ privacy-

Starting from the victim Facebook profile, we first identify his alter account on Twitter (if any), and then, thanks to interest assortativity, we are able to select some

We evaluate an OSN, where software agents represent users for privacy decisions and decide for sharing or not sharing co-owned content, according to our normative decision

The dialogue module will translate the argument from its computational shape (-1, Content B , Personal(0.6)) to a human readable text like ”It is highly recommended not to make

Our solution is elaborated from our mathematical model that allows to: (i) quantify the desired user location privacy and QoS, (ii) compute the silent and active periods that cope

There does not exist a correlation between the number of edge changes and the distance between graph clustering results: There is no guarantee that adding or removing l edges

Study 3 (Skinner et al, submitted to Microvascular Research) demonstrated that although neither blood rheological parameters nor microvascular function in-vivo were