Approximate Information Flows: Socially-Based Modeling of Privacy in Ubiquitous Computing

  • Xiaodong Jiang
  • Jason I. Hong
  • James A. Landay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2498)


In this paper, we propose a framework for supporting socially-compatible privacy objectives in ubiquitous computing settings. Drawing on social science research, we have developed a key objective called the Principle of Minimum Asymmetry, which seeks to minimize the imbalance between the people about whom data is being collected, and the systems and people that collect and use that data. We have also developed Approximate Information Flow (AIF), a model describing the interaction between the various actors and personal data. AIF effectively supports varying degrees of asymmetry for ubicomp systems, suggests new privacy protection mechanisms, and provides a foundation for inspecting privacy-friendliness of ubicomp systems.


Information Asymmetry Personal Data Asymmetric Information Ubiquitous Computing Information Space 
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

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Xiaodong Jiang
    • 1
  • Jason I. Hong
    • 1
  • James A. Landay
    • 1
  1. 1.Group for User Interface Research Computer Science DivisionUniversity of California BerkeleyBerkeleyUSA

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