Proceedings of the 17th International Conference on Data Engineering
Algorithms for Mining Association Rules for Binary Segmentations of Huge Categorical Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient Progressive Skyline Computation
Proceedings of the 27th International Conference on Very Large Data Bases
An optimal and progressive algorithm for skyline queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Catching the best views of skyline: a semantic approach based on decisive subspaces
VLDB '05 Proceedings of the 31st international conference on Very large data bases
DADA: a data cube for dominant relationship analysis
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Parallel Distributed Processing of Constrained Skyline Queries by Filtering
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Privacy-Preserving Skyline Queries in LBS
MVHI '10 Proceedings of the 2010 International Conference on Machine Vision and Human-machine Interface
Parallelizing skyline queries for scalable distribution
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Top-k combinatorial skyline queries
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
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Given a set of objects, a skyline query finds the objects that are not dominated by another object. A skyline query helps us to filter unnecessary information efficiently and gives us clues for various decision making tasks. On the other hand, we usually have to hide individual record's values even though there is no ID information in the table in privacy aware environments. In such situation, we cannot use conventional skyline queries. To handle the privacy problem, we considered a skyline query for sets of objects in a database. In which we do not disclose individual record's values. Let s be the number of objects in each set and n be the number of objects in the database. There are nCs sets in the database. We consider an efficient algorithm for computing convex skyline of the nCs sets, which we call 'convex skyline sets'. We further expand the idea of 'convex skyline sets' to use in a cloud computing environment in this paper. We propose a method for computing a skyline set query from distributed databases without disclosing individual records to others. There is no doubt that most of the cloud service providers do not want to disclose any individual record in their database. The proposed method utilises an agent computing framework and solves the privacy problems of skyline queries in cloud computing environments.