Identity federation systems enable participating organizations to provide services to qualified individuals and manage their identity attributes at an inter-organizational level. Most importantly, they empower individuals with control over the usage of their attributes within the federation via enforcement of various policies. Among such policies, one of the most important yet immature one is the privacy policy. Existing frameworks proposed for privacy-preserving federations lack the capability to support complex data-usage preferences in the form of obligations, i.e. the privacy related actions that must be performed upon certain actions on a specific piece of information. Moreover, they do not account for the history of events resulting from the interactions among federation entities.

Enterprises have been collecting data for many reasons including better customer relationship management, and high-level decision making. Public safety was another motivation for large-scale data collection efforts initiated by government agencies. However, such widespread data collection efforts coupled with powerful data analysis tools raised concerns about privacy. This is due to the fact that collected data may contain confidential information. One method to ensure privacy is to selectively hide confidential information from the data sets to be disclosed. In this paper, we focus on hiding confidential correlations. We introduce a heuristic to reduce the information loss and propose a blocking method that prevents discovery of confidential correlations while preserving the usefulness of the data set.