Composition and Disclosure of Unlinkable Distributed Databases

Abstract

An individualÕs location-visit pattern, or trail, can be leveraged to link sensitive data back to identity. We propose a secure multiparty computation protocol that enables locations to provably prevent such linkages. The protocol incorporates a controllable parameter specifying the minimum number of identities a sensitive piece of data must be linkable to via its trail.

Citation:
Bradley Malin and Latanya Sweeney. Composition and Disclosure of Unlinkable Distributed Databases. 22nd IEEE International Conference on Data Engineering, (ICDE). Atlanta, GA, April 2006. (PDF).

Related Links

• Trails Learning Project
• Other Projects at the Data Privacy Lab
• Data Privacy Laboratory