Abstract
Selecting representative objects from a large-scale dataset is an important task for understanding the dataset. Skyline is a popular technique for selecting representative objects from a large dataset. It is obvious that the skyline computation from the collective databases of multiple organizations is more effective than the skyline computed from a database of a single organization. However, due to privacy-awareness, every organization is also concerned about the security and privacy of their data. In this regards, we propose an efficient multi-party secure skyline computation method that computes the skyline on encrypted data and preserves the confidentiality of each party’s database objects. Although several distributed skyline computing methods have been proposed, very few of them consider the data privacy and security issues. However, privacy-preserving multi-party skyline computing techniques are not efficient enough. In our proposed method, we present a secure computation model that is more efficient in comparison with existing privacy-preserving multi-party skyline computation models in terms of computation and communication complexity. In our computation model, we also introduce MapReduce as a distributive, scalable, open-source, cost-effective, and reliable framework to handle multi-party data efficiently.
Highlights
In the present era of information technology, organizations with a similar type of service collect various information from their clients
The D&C algorithm divides the data in such a way that they can fit into memory; the candidate skylines are computed in each partition
The Branch-and-Bound Skyline (BBS) [13], proposed by Papadias et al, is a progressive algorithm based on the best-first nearest neighbor (BF-NN) algorithm
Summary
In the present era of information technology, organizations with a similar type of service collect various information from their clients. A number of skyline computation methods [1,2,4,5,6] utilize the MapReduce framework to calculate the skyline in a distributed environment, except [2], none of them consider the security issues for the multi-party skyline. This paper is organized as follows: Section 2 discusses and reviews the related work; Section 3 explains the required preliminary knowledge; Section 4 explains the methodology of computing the secure multi-party skyline with an example; Section 5 specifies the scalability and the application of the method; Section 6 specifies the security issues; Section 7 provides the theoretical analysis of our proposed method; Section 8 discusses the experiment details and explains the effectiveness and efficiency of our method under various settings; Section 9 concludes the proposed work
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