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Open AccessArticle

Privacy-Preserving Secure Computation of Skyline Query in Distributed Multi-Party Databases

1
Graduate School of Engineering, Hiroshima University, Higashi-Hiroshima 739-8527, Japan
2
Department of Computer Science and Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh
3
Department of Computer Science, Bogor Agricultural University, Bogor 1668, Indonesia
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper presented at the 12th International Conference on Advanced Data-Mining and Applications (ADMA 2016), Gold Coast, QLD, Australia, 12–15 December 2016. This version includes a more efficient implementation of our proposed method.
Information 2019, 10(3), 119; https://doi.org/10.3390/info10030119
Received: 21 February 2019 / Revised: 19 March 2019 / Accepted: 21 March 2019 / Published: 25 March 2019
Selecting representative objects from a large-scale database is an essential task to understand the database. A skyline query is one of the popular methods for selecting representative objects. It retrieves a set of non-dominated objects. In this paper, we consider a distributed algorithm for computing skyline, which is efficient enough to handle “big data”. We have noticed the importance of “big data” and want to use it. On the other hand, we must take care of its privacy. In conventional distributed algorithms for computing a skyline query, we must disclose the sensitive values of each object of a private database to another for comparison. Therefore, the privacy of the objects is not preserved. However, such disclosures of sensitive information in conventional distributed database systems are not allowed in the modern privacy-aware computing environment. Recently several privacy-preserving skyline computation frameworks have been introduced. However, most of them use computationally expensive secure comparison protocol for comparing homomorphically encrypted data. In this work, we propose a novel and efficient approach for computing the skyline in a secure multi-party computing environment without disclosing the individual attributes’ value of the objects. We use a secure multi-party sorting protocol that uses the homomorphic encryption in the semi-honest adversary model for transforming each attribute value of the objects without changing their order on each attribute. To compute skyline we use the order of the objects on each attribute for comparing the dominance relationship among the objects. The security analysis confirms that the proposed framework can achieve multi-party skyline computation without leaking the sensitive attribute value to others. Besides that, our experimental results also validate the effectiveness and scalability of the proposed privacy-preserving skyline computation framework. View Full-Text
Keywords: secure skyline; homomorphic encryption; Paillier cryptosystem; information security; data-mining; data privacy; semi-honest adversary model; multi-party computation secure skyline; homomorphic encryption; Paillier cryptosystem; information security; data-mining; data privacy; semi-honest adversary model; multi-party computation
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MDPI and ACS Style

Qaosar, M.; Zaman, A.; Siddique, M.A.; Annisa; Morimoto, Y. Privacy-Preserving Secure Computation of Skyline Query in Distributed Multi-Party Databases. Information 2019, 10, 119.

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