Mining multiple private databases using a kNN classifier

  • Xiong L
  • Chitti S
  • Liu L
  • 19


    Mendeley users who have this article in their library.
  • 20


    Citations of this article.


Modern electronic communication has collapsed geographical boundaries for global information sharing but often at the expense of data security and privacy boundaries. Distributed privacy preserving data mining tools are increasingly becoming critical for mining multiple databases with a minimum information disclosure. We present a framework including a general model as well as multi-round algorithms for mining horizontally partitioned databases using a privacy preserving k Nearest Neighbor (kNN) classifier. A salient feature of our approach is that it offers a trade-off between accuracy, efficiency and privacy through multi-round protocols. Copyright 2007 ACM.

Author-supplied keywords

  • Algorithms
  • Classification
  • Communication systems
  • Data mining
  • Data privacy
  • Distributed database systems
  • Distributed databases
  • Electronic communications
  • Global information sharing
  • Information retrieval
  • K nearest neighbor
  • K nearest neighbors
  • Multi round algorithms
  • Privacy

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • L Xiong

  • S Chitti

  • L Liu

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free