Privacy Aware Parallel Computation of Skyline Sets Queries from Distributed Databases

摘要

A skyline query finds the objects that are not dominated by another object from a given set of objects. Skyline queries help us to filter unnecessary information efficiently and provide us clues for various decision making tasks. However, we have to be aware about the privacy of individual's. In privacy aware environments, we have to hide individual records' values even though there is no ID information in the table. In such situations, it is not possible to use conventional skyline queries. To handle the privacy problem, we consider a skyline query for sets of objects in a database. Let s be the number of objects in each set and n be the number of objects in the database. The number of sets in the database is nCs. Skyline sets query returns all skyline s-sets from nCs sets. We consider an efficient algorithm for computing "convex" skyline of the nCs sets. In this paper, we propose a method for computing skyline sets queries in parallel fashion from distributed databases without disclosing individual records to others. The proposed method utilizes an agent-based parallel computing framework that solves the privacy problems of skyline queries in distributed environments. The computation of skyline sets is performed simultaneously in all databases that increases parallelism and reduces the computation time. We show through intensive experiments that our propose technique is almost independent of the number of servers involved in skyline sets queries, thus becoming an efficient and scalable solution for skyline sets queries in distributed environments.