Nowadays, a huge amount of data are available and shared in collaborative scenarios. These scenarios exist due to the need for joint computations of cooperating data owners for the purpose of making analysis and knowledge extraction. This requirement comes together with some privacy issues. One major issue is how to enable query execution, while no party is allowed to see the entire dataset (computational privacy). Thus, secure multiparty computation protocols allow a group of distrustful data owners to jointly cooperate in executing analytical queries against their data while revealing nothing about the entire dataset. In this paper, we propose a technique that enables a privacy-preserving query processing on horizontally partitioned electronic medical records among a set of hospitals, which have no desire to share their confidential data; however, they all need to cooperate to answer global queries about patients’ medical history. The proposed technique depends on a bucketization technique to reduce computational costs. It relies on a head party, which acts as a mediator between the authorized users and the cooperating parties, which are arranged in a star exchange topology. It ensures that the head party learns nothing about the sensitive data. Our experimental results prove that our technique provides a smaller computational cost and better privacy without the need for a trusted third party. |
