Approximate Information Flows: Socially-based Modeling of Privacy in Ubiquitous Computing
Xiaodong Jiang, Jason Hong, and James Landay
In this paper, we propose a framework for supporting sociallycompatible 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.