Privacy Preserving Collaborative Data Publishing

Privacy Preserving Collaborative Data Publishing

Organizations share data about their customers to explore potential business avenues. The sharing of data has posed several threats leading to individual identification. Owing to this, privacy preserving data publication has become an important research problem. The main goal of this problem is to preserve privacy of individuals while revealing useful information. An organization may implement and follow its privacy policy. But when two companies share information about a common set of individuals, and if their privacy policies differ, it is likely that there is privacy breach, unless there is a common policy. One solution was proposed for such scenario, based on k-anonymity and cut-tree method for 2-party data. This paper suggests a simple solution for integrating n-party data using dynamic programming on subsets. The solution is based on thresholds for privacy and informativeness based on k-anonymity.