• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE Conference on Communications and Network Security >A correlation-based subspace analysis for data confidentiality and classification as utility in CPS
【24h】

A correlation-based subspace analysis for data confidentiality and classification as utility in CPS

机译：基于相关的子空间分析，用于数据机密性和分类作为CPS的效用

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

摘要

The concept of pairing confidential-relevant variables (connected variables) using ridge regression and bootstrap sampling has recently been proposed for developing perturbation models to data privacy in cyber-physical systems. In this approach, a single set of perturbation parameters for all the pairs of connected variables has been used to achieve trade-off between data confidentiality and classification as data utility. It has led to weaker confidentiality protection for some pairs of connected variables than the others. In this paper, we have determined that this discrepancy occurs due to varying correlation characteristics between the variables. The correlation between a connected variable and other confidential variables influences the correctness of the perturbation parameters of the ridge regression model studied for data privacy. In this paper, we have divided the feature space into correlated subspaces and studied the ridge regression-based perturbation model with bootstrap sampling in individual subspaces separately. Our experimental analysis with IRIS and NSL-KDD datasets has provided an interesting finding, indicating that the absolute Pearson correlation coefficient greater than 0.1, between the connected and confidential variables, can lead to strong confidentiality, as measured by signal-interference-ratio less than 20dB.
机译：最近已经提出了使用RIDGE回归和引导抽样进行配对机密相关变量（连接变量）的概念，用于开发扰动模型以在网络物理系统中的数据隐私。在这种方法中，所有对连接变量的单组扰动参数都已用于在数据机密性和分类之间实现权衡作为数据实用程序。它导致对某些连接的变量比其他成对的密钥保护较弱。在本文中，我们已经确定了由于变量之间的相关特性而发生这种差异。连接变量与其他机密变量之间的相关性影响研究数据隐私的脊回归模型的扰动参数的正确性。在本文中，我们将特征空间划分为相关的子空间，并在单独的子空间中使用自举对基于Ride回归的扰动模型进行了研究。我们的实验分析和NSL-KDD数据集提供了一个有趣的发现，表明，在连接和机密变量之间的绝对Pearson相关系数大于0.1，可以导致强机密度，通过信号干扰比少于20dB。

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