Privacy-preserving multivariate statistical analysis: Linear regression and classification

Wenliang Du, Yunghsiang S. Han, Shigang Chen

Research output: Contribution to conferencePaper

157 Scopus citations

Abstract

Multivariate statistical analysis is an important data analysis technique that has found applications in various areas. In this paper, we study some multivariate statistical analysis methods in Secure 2-party Computation (S2C) framework illustrated by the following scenario: two parties, each having a secret data set, want to conduct the statistical analysis on their joint data, but neither party is willing to disclose its private data to the other party or any third party. The current statistical analysis techniques cannot be used directly to support this kind of computation because they require all parties to send the necessary data to a central place. In this paper, We define two Secure 2-party multivariate statistical analysis problems: Secure 2-party Multivariate Linear Regression problem and Secure 2-party Multivariate Classification problem. We have developed a practical security model, based on which we have developed a number of building blocks for solving these two problems.

Original languageEnglish (US)
Pages222-233
Number of pages12
DOIs
StatePublished - 2004
EventProceedings of the Fourth SIAM International Conference on Data Mining - Lake Buena Vista, FL, United States
Duration: Apr 22 2004Apr 24 2004

Other

OtherProceedings of the Fourth SIAM International Conference on Data Mining
CountryUnited States
CityLake Buena Vista, FL
Period4/22/044/24/04

Keywords

  • Multivariate statistical analysis
  • Privacy
  • Secure multi-party computation
  • Security

ASJC Scopus subject areas

  • Mathematics(all)

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    Du, W., Han, Y. S., & Chen, S. (2004). Privacy-preserving multivariate statistical analysis: Linear regression and classification. 222-233. Paper presented at Proceedings of the Fourth SIAM International Conference on Data Mining, Lake Buena Vista, FL, United States. https://doi.org/10.1137/1.9781611972740.21