PRIVACY-PRESERVING OLAP FOR ACCURATE ANSWER

Recently, growing privacy concerns have received more and more attention and it becomes a significant topic on how to preserve private-sensitive information from being violated in distributed cooperative computation. In this paper, we first propose a novel-general privacy-preserving online analytical processing model based on secure multiparty computation. Then, based on the new model, two schemes to privacy-preserving count aggregate query over both horizontally partitioned data and vertically partitioned data are proposed. Additionally, we also propose several efficient subprotocols that serve as the basic secure buildings. Furthermore, we analyze correctness, security, communication cost, and computation complexity of our proposed protocols, and show that the new schemes are secure, having good linear complexity and that the query results are exactly accurate.