Preserving privacy in social network integration with τ-tolerance

Social network analysis and mining is very useful for law enforcement and intelligence to extract criminals or terrorists interaction patterns and identify their roles in the organizations. Due to the privacy concerns, social network data is usually captured within a law enforcement or intelligence unit without sharing with other units. As a result, the utility of social network analysis is diminished when the social network data within an individual unit is incomplete. In this project, the objectives are sharing the insensitive and generalized information to support social network analysis and mining but preserving the privacy at the same time. We ensure that a prescribed level of privacy leakage tolerance is satisfied. The measurement of the privacy leakage is independent to the privacy preserving techniques of integrating social network data.