Privacy-preserving data publishing for multiple numerical sensitive attributes

Qinghai Liu, Hong Shen, Yingpeng Sang

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)


Anonymized data publication has received considerable attention from the research community in recent years. For numerical sensitive attributes, most of the existing privacy-preserving data publishing techniques concentrate on microdata with multiple categorical sensitive attributes or only one numerical sensitive attribute. However, many real-world applications can contain multiple numerical sensitive attributes. Directly applying the existing privacy-preserving techniques for single-numerical-sensitive-attribute and multiple-categorical-sensitive-attributes often causes unexpected disclosure of private information. These techniques are particularly prone to the proximity breach, which is a privacy threat specific to numerical sensitive attributes in data publication. In this paper, we propose a privacy-preserving data publishing method, namely MNSACM, which uses the ideas of clustering and Multi-Sensitive Bucketization (MSB) to publish microdata with multiple numerical sensitive attributes. We use an example to show the effectiveness of this method in privacy protection when using multiple numerical sensitive attributes.

Original languageEnglish
Article number7128936
Pages (from-to)246-254
Number of pages9
JournalTsinghua Science and Technology
Issue number3
Publication statusPublished - 1 Jun 2015
Externally publishedYes


  • Multi-Sensitive Bucketization (MSB)
  • clustering
  • k-anonymity
  • numerical sensitive attribute
  • privacy-preserving


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