ABACUS: A distributed middleware for privacy preserving data sharing across private data warehouses

Abstract

Recent trends in the global economy force competitive enterprises to collaborate with each other to analyze markets in a better way and make decisions based on that. Therefore, they might want to share their data with each other to run data mining algorithms over the union of their data to get more accurate and representative results. During this process they do not want to reveal their data to each other due to the legal issues and competition. However, current systems do not consider privacy preservation in data sharing across private data sources. To satisfy this requirement, we propose a distributed middleware, ABACUS, to perform intersection, join, and aggregation queries over multiple private data warehouses in a privacy preserving manner. Our analytical evaluations show that ABACUS is efficient and scalable. © IFIP International Federation for Information Processing 2005.