Enterprise privacy promises and enforcement

  • Barth A
  • Mitchell J
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Abstract

Several formal languages have been proposed to encode pri-vacy policies, ranging from the Platform for Privacy Pref-erences (P3P), intended for communicating privacy policies to consumers over the web, to the Enterprise Privacy Au-thorization Language (EPAL), intended to enable policy en-forcement within an enterprise. However, current technol-ogy does not allow an enterprise to determine whether its detailed, internal enforcement policy meets its published pri-vacy promises. We present a data-centric, unified model for privacy, equipped with a modal logic for reasoning about permission inheritance across data hierarchies. We use this model to critique two privacy preference languages (APPEL and XPref), to justify P3P's policy summarization algo-rithm, and to connect privacy policy languages, such as P3P, with privacy policy enforcement languages, such as EPAL. Specifically, we characterize when one policy enforces an-other and provide an algorithm for generating the most spe-cific privacy promises, at a given level of detail, guaranteed by a more detailed enforcement policy.

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Authors

  • Adam Barth

  • John C. Mitchell

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