Fragments and Loose Associations: Respecting Privacy in Data Publishing

We propose a modeling of the problem of privacy-compliant data publishing that captures confidentiality constraints on one side and visibility requirements on the other side. Confidentiality constraints express the fact that some attributes, or associations among them, are sensitive and cannot be released. Visibility requirements express requests for views over data that should be provided. We propose a solution based on data fragmentation to split sensitive associations while ensuring visibility. In addition, we show how sensitive associations broken by fragmentation can be released in a sanitized form as loose associations formed in a way to guarantee a specified degree of privacy.