Secure k-skyband computation framework in distributed multi-party databases

Abstract In this paper, we propose a secure k-skyband computation framework in distributed multi-party databases, in which we can compute multi-party k-skyband without disclosing attributes of each object in a party to other parties, which is essential in privacy-aware applications. Although several secure skyline computation algorithms have been proposed, all of the conventional methods require a large amount of communication and computationally expensive secure comparison. Due to the large computational and communication complexities, they are not suitable for comparing or querying over a significant number of encrypted objects, which are located in privacy-preserving distributed multi-party databases. In the proposed framework, we used a secure multi-party sorting method that uses a homomorphic encryption scheme in the semi-honest adversary model and then uses the sorting order of the objects’ attributes on each dimension for k-skyband computation. The detailed security analysis shows that the proposed framework can achieve secure multi-party k-skyband computation without leaking sensitive information to others. Besides that, the performance evaluations via extensive simulations also demonstrate the efficiency of our proposed framework.

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