This paper 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.
& Chen, K.
(2012). Privacy Preserving Boosting in the Cloud with Secure Half-Space Queries. Proceedings of the 2012 ACM Conference on Computer and Communications Security, 1031-1033.