• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE transactions on dependable and secure computing >Privacy-Preserving Multi-Keyword Top-$k$ k
【24h】

Privacy-Preserving Multi-Keyword Top-$k$ k

机译：隐私保留多关键词顶部 - <内联公式> $ k $ <替代品> k

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Cloud computing provides individuals and enterprises massive computing power and scalable storage capacities to support a variety of big data applications in domains like health care and scientific research, therefore more and more data owners are involved to outsource their data on cloud servers for great convenience in data management and mining. However, data sets like health records in electronic documents usually contain sensitive information, which brings about privacy concerns if the documents are released or shared to partially untrusted third-parties in cloud. A practical and widely used technique for data privacy preservation is to encrypt data before outsourcing to the cloud servers, which however reduces the data utility and makes many traditional data analytic operators like keyword-based top-k document retrieval obsolete. In this paper, we investigate the multi-keyword top-k search problem for big data encryption against privacy breaches, and attempt to identify an efficient and secure solution to this problem. Specifically, for the privacy concern of query data, we construct a special tree-based index structure and design a random traversal algorithm, which makes even the same query to produce different visiting paths on the index, and can also maintain the accuracy of queries unchanged under stronger privacy. For improving the query efficiency, we propose a group multi-keyword top-k search scheme based on the idea of partition, where a group of tree-based indexes are constructed for all documents. Finally, we combine these methods together into an efficient and secure approach to address our proposed top-k similarity search. Extensive experimental results on real-life data sets demonstrate that our proposed approach can significantly improve the capability of defending the privacy breaches, the scalability and the time efficiency of query processing over the state-of-the-art methods.
机译：云计算为个人和企业提供了大量的计算能力和可扩展的存储容量，以支持域中的各种大数据应用，如医疗保健和科学研究，因此越来越多的数据所有者都参与了在云服务器上外包的数据，以便在数据方面提供极大的便利管理和采矿。然而，电子文档中的健康记录等数据集通常包含敏感信息，如果文件被释放或分享到云中部分不受信任的第三方，则会带来隐私问题。用于数据隐私保存的实用和广泛使用的技术是在外包到云服务器之前加密数据，但是减少了数据实用程序，并使许多传统数据分析运算符如基于关键字的Top-K文档检索已过时。在本文中，我们调查了对隐私漏洞的大数据加密的多关键字Top-K搜索问题，并尝试识别对此问题的高效和安全解决方案。具体而具体来说，对于查询数据的隐私问题，我们构建了一种基于特殊的树索引结构并设计了一种随机遍历算法，这使得同一查询甚至可以在索引上产生不同的访问路径，并且还可以保持查询的准确性不变在更强大的隐私下。为了提高查询效率，我们提出了一种基于分区的概念的组多关键字Top-k搜索方案，其中为所有文档构建了一组基于树的索引。最后，我们将这些方法结合在一起，以有效和安全的方法来解决我们提出的Top-K类似搜索。实验数据集的广泛实验结果表明，我们的建议方法可以显着提高捍卫隐私违规的能力，通过最先进的方法捍卫Query处理的可扩展性和时间效率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号