Privacy-preserving k-means clustering over vertically partitioned data

  • Authors:
  • Jaideep Vaidya;Chris Clifton

  • Affiliations:
  • Purdue University, West Lafayette, IN;Purdue University, West Lafayette, IN

  • Venue:
  • Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
  • Year:
  • 2003

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Abstract

Privacy and security concerns can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. The key is to obtain valid results, while providing guarantees on the (non)disclosure of data. We present a method for k-means clustering when different sites contain different attributes for a common set of entities. Each site learns the cluster of each entity, but learns nothing about the attributes at other sites.