Privacy preserving boosting in the cloud with secure half-space queries

Authors:
Shumin Guo;Keke Chen
Affiliations:
Wright State University, Dayton, OH, USA;Wright State University, Dayton, OH, USA
Venue:
Proceedings of the 2012 ACM conference on Computer and communications security
Year:
2012

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Abstract

This poster presents a preliminary study on the PerturBoost approach that aims to provide efficient and secure classifier learning in the cloud with both data and model privacy preserved.