J4 ›› 2011, Vol. 46 ›› Issue (5): 82-85.

• Articles • Previous Articles     Next Articles

Privacy preserving approaches for relational multiple sensitive attributes

LI Li, YUAN Fang, XI Ya-hui   

  1. College of Mathematics and Computer Science, Hebei University, Baoding 071000, Hebei, China
  • Received:2010-12-06 Published:2011-05-25


Directly applying the existing sensitive attribute privacy protection methods to multiple sensitive attributes privacy protection can divulge the privacy data. Firstl, the thought of lossy join to protect privacy data was inherited, and the records of tables were clustered to guarantee the sensitive rank division in tables. Then, the records were grouped according to the frequency of comparative strategy. And a grouping algorithm aimed at data containing multi-sensitive attributes was proposed based on the cluster. Experimental results indicated that this algorithm could prevent the privacy revelation and strengthen the security of data published.

Key words: data privacy; relational multi-sensitive attribute; k-diversity; independent relational; joint relational

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