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An efficient quasi-identifier index based approach for privacy preservation over incremental data sets on cloud

机译：一种基于准标识符索引的高效方法，用于在云上增量数据集上进行隐私保护

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摘要

Cloud computing provides massive computation power and storage capacity which enable users to deploy applications without infrastructure investment. Many privacy-sensitive applications like health services are built on cloud for economic benefits and operational convenience. Usually, data sets in these applications are anonymized to ensure data owners' privacy, but the privacy requirements can be potentially violated when new data join over time. Most existing approaches address this problem via re-anonymizing all data sets from scratch after update or via anonymizing the new data incrementally according to the already anonymized data sets. However, privacy preservation over incremental data sets is still challenging in the context of cloud because most data sets are of huge volume and distributed across multiple storage nodes. Existing approaches suffer from poor scalability and inefficiency because they are centralized and access all data frequently when update occurs. In this paper, we propose an efficient quasi-identifier index based approach to ensure privacy preservation and achieve high data utility over incremental and distributed data sets on cloud. Quasi-identifiers, which represent the groups of anonymized data, are indexed for efficiency. An algorithm is designed to fulfil our approach accordingly. Evaluation results demonstrate that with our approach, the efficiency of privacy preservation on large-volume incremental data sets can be improved significantly over existing approaches.
机译：云计算提供了巨大的计算能力和存储容量，使用户无需基础设施投资即可部署应用程序。为了经济利益和操作便利，许多对隐私敏感的应用程序（例如健康服务）都构建在云上。通常，这些应用程序中的数据集是匿名的，以确保数据所有者的隐私，但是随着时间的推移加入新数据时，可能会违反隐私要求。大多数现有方法是通过在更新后从头开始对所有数据集重新进行匿名化，或者根据已经匿名化的数据集对新数据进行增量化匿名化来解决此问题。但是，在云环境中，对增量数据集进行隐私保护仍然是一项挑战，因为大多数数据集的数量巨大并且分布在多个存储节点上。现有方法存在可伸缩性和效率低下的问题，因为它们被集中并且在更新发生时频繁访问所有数据。在本文中，我们提出了一种基于准标识符索引的有效方法，可确保隐私保护并在云上的增量和分布式数据集上实现较高的数据实用性。代表匿名数据组的准标识符被索引以提高效率。设计了一种算法来相应地实现我们的方法。评估结果表明，采用我们的方法，与现有方法相比，可以大幅度提高对大量增量数据集进行隐私保护的效率。

著录项

  • 来源
    《Journal of computer and system sciences》 |2013年第5期|542-555|共14页
  • 作者

    Xuyun Zhang; Chang Liu; Surya Nepal; Jinjun Chen;

  • 作者单位

    Faculty of Engineering and Information Technology, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia;

    Faculty of Engineering and Information Technology, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia;

    Centre for Information & Communication Technologies, Commonwealth Scientific and Industrial Research Organisation, Cnr Vimiera and Pembroke Rodas Marsfield,NSW 2122, Australia;

    Faculty of Engineering and Information Technology, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    cloud computing; privacy preservation; incremental data set; anonymization; quasi-identifier index;

    机译：云计算;隐私保护;增量数据集;匿名化;准标识符索引;

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