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Who Got All of My Personal Data? Enabling Users to Monitor the Proliferation of Shared Personally Identifiable Information

  • Sebastian Labitzke
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 375)

Abstract

The risk involved when users publish information, which becomes available to an unintentional broad audience via online social networks is evident. It is especially difficult for users of social networks to determine who will get the information before it is shared. Moreover, it is impossible to monitor data flows or to control the access to personal data after sharing the information. In contrast to enterprise identity management systems, in which provider-engineered processes control the access to and flow of data, the users of social networks themselves are responsible for information management. Consequently, privacy requirements have become important so that users can control the flow of their personal data across social networks and beyond. In particular, this kind of user-based information management should provide the capability to control data flows in a proactive manner, as well as reactive components to monitor the proliferation of data. In this conceptual paper, we motivate the necessity of a dedicated user-based information management on the basis of studies that we conducted on information that users share publicly in online social networks. Moreover, we outline the building blocks of user-based information management on the basis of existing approaches, which support users in managing data flows and an investigation that we did on the linkability of social network profiles. Furthermore, we contrast user-based information management with our experiences in developing and operating federated identity management services at the Karlsruhe Institute of Technology (KIT).

Keywords

Information Management Personal Data Identity Management Reactive Component Online Social Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Sebastian Labitzke
    • 1
  1. 1.Steinbuch Centre for Computing (SCC) & Institute of TelematicsKarlsruhe Institute of Technology (KIT)KarlsruheGermany

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