A new scheme on privacy-preserving classification

Nan Zhang, Shengquan Wang, Wei Zhao

Research output: Contribution to conferencePaperpeer-review

49 Scopus citations

Abstract

We address privacy-preserving classification problem in a distributed system. Randomization has been the approach proposed to preserve privacy in such scenario. However, this approach is now proven to be insecure as it has been discovered that some privacy intrusion techniques can be used to reconstruct private information from the randomized data tuples. We introduce an algebraic-technique-based scheme. Compared to the randomization approach, our new scheme can build classifiers more accurately but disclose less private information. Furthermore, our new scheme can be readily integrated as a middleware with existing systems.

Original languageEnglish (US)
Pages374-383
Number of pages10
StatePublished - 2005
EventKDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Chicago, IL, United States
Duration: Aug 21 2005Aug 24 2005

Other

OtherKDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Country/TerritoryUnited States
CityChicago, IL
Period8/21/058/24/05

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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