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  • 标题:A Review on Temporal Data Clustering With Unlike Representations
  • 本地全文:下载
  • 作者:P.Anusha ; K. Sudheer Kumar ; Dr. R.V.Krishnaiah
  • 期刊名称:International Journal of Computer Science & Technology
  • 印刷版ISSN:2229-4333
  • 电子版ISSN:0976-8491
  • 出版年度:2013
  • 卷号:4
  • 期号:1
  • 页码:492-494
  • 语种:English
  • 出版社:Ayushmaan Technologies
  • 摘要:Privacy preserving data publishing is a promising research area since individual privacy has become a major concern. Privacypreserving techniques are often required to reduce the possibility of identifying sensitive information about individuals, when a data set is released to other parties for data analysis. Sensitive information can be a specific location of an individual for spatial data. The study is made on protecting the privacy of persons and data has received from many fields. The information that a user browses certain websites may be considered sensitive in web surfing. We review the research work in privacy-preserving data publishing. Without violating the confidentiality of personal information we explain how an organization can release the data to the public is discussed. However, the simplest solution to protect the sensitive information is not to disclose the information but it will delay the data analysis process. The data must be disclosed under the government regulations in some applications. On the other hand, if the data owner first modifies the data then the modified data can guarantee privacy, such that the modified data retains enough utility and can be released to other parties safely. In this paper, we examine how the data owner can modify the data and how the modified data can preserve privacy and protect sensitive information.
  • 关键词:Privacy Preservation;Sensitive Information;Data Publishing; Data Analysis Process;Spatial Data
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