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Knowledge transfer-based distributed differential evolution for dynamic database fragmentation
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DOI：10.1016/j.knosys.2021.107325.png)
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Abstract
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Database fragmentation can protect the distributed database's privacy by dividing attributes of sensitive associations into different fragments. Previous database fragmentation algorithms are designed for the initialization of the distributed database. However, the initial database fragmentation cannot maintain its effect during the distributed database's entire life cycle. This paper defines a dynamic database fragmentation problem with privacy preservation requirements, in which both the privacy preservation degree and the communication cost are considered during the optimization. For this problem, a knowledge transfer-based distributed differential evolution algorithm (KT-DDE) is proposed to achieve the optimal communication cost and maintain privacy preservation. The proposed KT-DDE algorithm includes a distributed framework and a differential evolution-based optimizer. In the proposed distributed framework, the fragmentation knowledge is transferred between different database fragmentation subproblems. The fragmentation information of various individuals is exchanged in the optimizer and used to generate trial individuals. After the selection, competitive trial individuals are kept in the population. Experimental results show that the proposed algorithm can outperform the other competitors in terms of solution accuracy, convergence speed, and computation efficiency. In addition, the effectiveness of the proposed components is verified. (C) 2021 Published by Elsevier B.V.
Keywords:
Distributed differential evolution
Knowledge transfer
Dynamic database fragmentation
Database privacy and utility
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