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      Distributed Memetic Algorithm for Outsourced Database Fragmentation.

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          Abstract

          Data privacy and utility are two essential requirements in outsourced data storage. Traditional techniques for sensitive data protection, such as data encryption, affect the efficiency of data query and evaluation. By splitting attributes of sensitive associations, database fragmentation techniques can help protect data privacy and improve data utility. In this article, a distributed memetic algorithm (DMA) is proposed for enhancing database privacy and utility. A balanced best random distributed framework is designed to achieve high optimization efficiency. In order to enhance global search, a dynamic grouping recombination operator is proposed to aggregate and utilize evolutionary elements; two mutation operators, namely, merge and split, are designed to help arrange and create evolutionary elements; a two-dimension selection approach is designed based on the priority of privacy and utility. Furthermore, a splicing-driven local search strategy is embedded to introduce rare utility elements without violating constraints. Extensive experiments are carried out to verify the performance of the proposed DMA. Furthermore, the effectiveness of the proposed distributed framework and novel operators is verified.

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          Author and article information

          Journal
          IEEE Trans Cybern
          IEEE transactions on cybernetics
          Institute of Electrical and Electronics Engineers (IEEE)
          2168-2275
          2168-2267
          Nov 04 2020
          : PP
          Article
          10.1109/TCYB.2020.3027962
          33147158
          0137c441-ae2f-4dc4-93b4-62950dfb4e9b
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