• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International WIE Conference on Electrical and Computer Engineering >Prognosis using distributed data classification with privacy preserving: A novel approach
【24h】

Prognosis using distributed data classification with privacy preserving: A novel approach

机译：使用具有隐私保护功能的分布式数据分类进行预后：一种新方法

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Privacy preserving data mining (PPDM) is a captivating forte for every researcher who has been closely pursuing data mining, for its inherent nature of ubiquitous pervasiveness. As few years back, data mining was essential and vital to any sphere, so is the now the spectrum of privacy preserving data mining expanding with a thrust upon its applicability and efficacy. PPDM is a pool of solutions which takes care of shielding of data which has personal or private information and where any level of percolation of such information can be a cause of colossal and irreversible loss to an individual or business. At the same time, PPDM is also concerned with not compromising on the utility of other data which would be participating in mining. A balance between both the aspects: the secrecy and accuracy requires a smart balancing solution. Any algorithm suggested vary in several measures like efficiency, accuracy, data transfer costs, level of secrecy maintained, speed: to name a few. No algorithm is such that it can be generalized to perform superior to the rest. They are situation, domain and requirement specific. In this paper, an algorithm with a background framework for PPDM is proposed which anonymizes sensitive horizontal partitioned style distributed data, before they partake in collective mining process. Efforts have been made to conceal maximum personal information and not allowing it to affect on the results of mining. It is also kept in mind that the data transfer remains minimal during the entire process without distressing the quality of final findings. The experimental results and analysis is also presented for a detailed evaluation of the proposed method. An earlier solution in the same genre and environment is compared with the existing solution on important aspects.
机译：隐私保护数据挖掘（PPDM）是每位一直致力于数据挖掘的研究人员着迷的特长，因为它具有无处不在的普遍性。几年前，数据挖掘对于任何领域都是必不可少的，而且至关重要。如今，隐私保护数据挖掘的范围也在不断扩大，并着眼于其适用性和有效性。 PPDM是一个解决方案池，用于保护具有个人或私人信息的数据，并且这些信息的任何级别的渗透都可能对个人或企业造成巨大和不可逆转的损失。同时，PPDM还担心不影响将要参与挖掘的其他数据的实用性。这两个方面之间的平衡：保密性和准确性需要一个智能的平衡解决方案。所建议的任何算法都在几种度量上有所不同，例如效率，准确性，数据传输成本，保密级别，速度：仅举几例。没有一种算法可以概括为执行优于其他算法的算法。它们是针对具体情况，领域和需求的。本文提出了一种具有背景框架的PPDM算法，该算法在敏感的水平分区样式分布式数据参与集体挖掘之前将其匿名化。已努力隐藏最大的个人信息，并且不允许其影响挖掘的结果。还应记住，在整个过程中数据传输保持最少，而不会影响最终结果的质量。还提供了实验结果和分析，以对所提出的方法进行详细评估。将相同类型和环境的早期解决方案与重要方面的现有解决方案进行比较。

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