IEEE Trans Cybern. 2021 Oct;51(10):4808-4821. doi: 10.1109/TCYB.2020.3027962. Epub 2021 Oct 12.
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.
数据隐私和效用是外包数据存储的两个基本要求。传统的敏感数据保护技术，如数据加密，会影响数据查询和评估的效率。通过对敏感关联的属性进行分割，数据库分片技术可以帮助保护数据隐私并提高数据效用。本文提出了一种分布式遗传算法（DMA）来增强数据库的隐私性和效用。设计了一个平衡的最佳随机分布式框架来实现高效的优化。为了增强全局搜索能力，提出了一种动态分组重组算子来聚合和利用进化元素；设计了两种突变算子，即合并和分裂，以帮助排列和创建进化元素；设计了一种基于隐私和效用优先级的二维选择方法。此外，嵌入了拼接驱动的局部搜索策略，以在不违反约束的情况下引入罕见的效用元素。进行了广泛的实验来验证所提出的 DMA 的性能。此外，还验证了所提出的分布式框架和新颖算子的有效性。
