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Privacy preserving big data publishing: a scalable k-anonymization approach using MapReduce

机译：隐私保护大数据发布：使用MapReduce的可扩展k匿名方法

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摘要

Big data is collected and processed using different sources and tools that lead to privacy issues. Privacy preserving data publishing techniques such as k-anonymity, l-diversity, and t-closeness are used to de-identify the data; however, the chances of re-identification are always remain present since data is collected from multiple sources. Owing to the large volume of data, less generalisation or suppression is required to achieve the same level of privacy, which is also known as ‘large crowd effect’, although it is always challenging to handle such a large data for anonymization. MapReduce handles large volume of data and distributes the data into the smaller chunks across the multiple nodes; consequently, the full advantage of large volume of data is underachieved. Therefore, scalability of privacy preserving techniques becomes a challenging area of research. The authors explore this area and propose an algorithm named scalable k-anonymization (SKA) using MapReduce for privacy preserving big data publishing. The authors also compare the approach with existing approaches that results into a remarkable improvement of the data utility and significantly enhances the performance in terms of running time.
机译：使用导致隐私问题的不同来源和工具来收集和处理大数据。保留隐私的数据发布技术（例如k匿名性，l分集和t紧密度）用于识别数据；但是，由于数据是从多个来源收集的，因此始终存在重新识别的机会。由于数据量大，因此实现相同级别的隐私需要较少的泛化或抑制，这也被称为“大人群效应”，尽管处理如此大的数据以进行匿名化始终具有挑战性。 MapReduce处理大量数据，并将数据分布到多个节点中的较小块中。因此，无法实现海量数据的全部优势。因此，隐私保护技术的可扩展性成为研究的挑战领域。作者探索了这一领域，并提出了一种使用MapReduce的可伸缩k匿名化（SKA）算法，用于保护大数据发布的隐私。作者还将该方法与现有方法进行了比较，从而显着改善了数据实用程序并在运行时间方面显着提高了性能。

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