Citizen in Sensor Networks: First International Workshop, by Jordi Nin, Daniel Villatoro

By Jordi Nin, Daniel Villatoro

This publication constitutes the completely refereed post-conference complaints of the 1st overseas Workshop on Citizen Sensor Networks, CitiSens 2012, in Montpellier, France, on August 27, 2012.
The 7 revised complete papers provided including 1 keynote lectures have been conscientiously reviewed and chosen from sixteen submissions. The authorized papers care for themes like crowdsourcing, shrewdpermanent towns, multi-agent structures, privateness in social networks, facts anonymity or shrewdpermanent sensors.

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Extra info for Citizen in Sensor Networks: First International Workshop, CitiSens 2012, Montpellier, France, August 27, 2012, Revised Selected Papers

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Information loss [17] checks the quantity of data that has been harmed during the protection process and therefore is no longer useful. Disclosure risk [6][21][22] measures the quantity of original data that can be discovered through the protected data. The remaining of this paper is structured as follows. In Section 2 we explain the methodology followed to go from a real social network like Twitter to obtaining a microdata dataset with explicit and implicit information about users. Section 3 contains the description of the protection method used in this work to protect the microdata dataset: the microaggregation.

2 User Profiles Generation The second step to do is to use the data structures collected by the crawler in order to get a profile for each user containing his location, his connected users and, his three most relevant topics of interest. In order to do this it should be noticed that information is not always explicitly given in the social networks. That is, using the Twitter API we can get the location but it is not possible to get the topics that a user is interested about because they are not specified nor described anywhere.

This lack of possible operations makes the protection a difficult task. Protection methods are typically evaluated using two measures: information loss and disclosure risk. Information loss [17] checks the quantity of data that has been harmed during the protection process and therefore is no longer useful. Disclosure risk [6][21][22] measures the quantity of original data that can be discovered through the protected data. The remaining of this paper is structured as follows. In Section 2 we explain the methodology followed to go from a real social network like Twitter to obtaining a microdata dataset with explicit and implicit information about users.

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