Privacy-Aware Social Mining

Mobility is on everybody’s lips in our computer science community, and beyond. It is one of the major keywords that characterize the current development of our society. People, goods, and ideas are moving faster and more frequently than ever. All services are getting as mobile as their customers. This entails a considerable boom in all logistics geared to provide ever-increasing facilities to support mobility. Be it for the development of contextualized information services (e.g. location-based systems) or the capture, management and exploitation of movement data (generated via the use of GPS, sensors, RFIDs, cellphones, smartphones, and alike), a simple look at conferences titles and programs shows that mobility is a hot topic with plenty of possibilities for innovative research. Even Google is sponsoring a mobility data contest.

In response to the mobility challenge, several research communities are investigating new techniques suitable for extracting meaningful knowledge from tracks of moving objects enriched with contextual data, with the goal to enable a richer use of movement data. Tracking of moving objects produces huge amounts of data. Yet these data (basically GPS records) do not readily convey information about their meaning from an application viewpoint. Hence their use remains quite limited. While previous research mainly focused on cleaning and processing the data received from sensors, GPS devices and alike, in order to produce better movement tracks, recent research rather focuses on methods to enrich a movement track with more semantic, application-oriented information. Turning mobility data closer to application requirements has indeed a huge potential for innovative uses and far reaching analyses.

One concern that is extremely important when dealing with human mobility tracks is privacy. To what extent can we develop techniques that guarantee no disclosure of sensitive information to unauthorized persons? How can the new techniques cope with the fact that the level of sensitivity of information may depend on many different factors, including where and when the information is accessed, whose information it is, and who is requesting access.

This summer school provides an intensive training opportunity to learn the essentials of recent research on of mobility data management while exploring other related domains that tomorrow will contribute in advancing the state of art in mobility techniques.

Student will follow lectures from best experts, receive personalized training on selected exercises, and benefit from hands-on sessions on mobility research prototypes. They will personally experiment mobility related tasks to practice with the concepts from the lessons.

MODAP Community

Click here to go to the members are of the 2nd MODAP-MOVE Summer School.

If you want to join MODAP Community, please send an email to Yucel Saygin () with a few lines explaining your interest in MODAP

European Cooperation in Science and Technology