Secure Multi-Party linear Regression

نویسندگان

  • Fida Kamal Dankar
  • Renaud Brien
  • Carlisle M. Adams
  • Stan Matwin
چکیده

Increasing efficiency in hospitals is of particular importance. Studies that combine data from multiple hospitals/data holders can tremendously improve the statistical outcome and aid in identifying efficiency markers. However, combining data from multiple sources for analysis poses privacy risks. A number of protocols have been proposed in the literature to address the privacy concerns; however they do not fully deliver on either privacy or complexity. In this paper, we present a privacy preserving linear regression model for the analysis of data coming from several sources. The protocol uses a semi-trusted third party and delivers on privacy and complexity.

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تاریخ انتشار 2014