Paper Info
Title
Privacy Aware Parallel Computation of Skyline Sets Queries from Distributed Databases.
Abstract
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.
Year
DOI
Venue
2011
10.1109/ICNC.2011.35
Computing and Informatics
Keywords
DocType
Volume
conventional skyline query,skyline set,skyline s-sets,skyline query,privacy problem,skyline sets query,skyline sets queries,agent-based parallel computing framework,privacy aware environment,privacy aware parallel computation,ncs set,individual record,parallel computer,object oriented programming,data privacy,parallel computation,distributed database,parallel processing,distributed environment,distributed databases
Conference
33
Issue
ISSN
ISBN
4
1335-9150
978-1-4577-1796-3
Citations 
PageRank 
References 
1
0.35
16
Authors
2
Name
Order
Citations
PageRank
Mohammad Shamsul Arefin12613.23
Yasuhiko Morimoto2528341.88