Document Type
Conference Proceeding
Publication Date
10-2012
Abstract
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.
Repository Citation
Guo, S.,
& 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.
https://corescholar.libraries.wright.edu/knoesis/909
DOI
10.1145/2382196.2382315
Included in
Bioinformatics Commons, Communication Technology and New Media Commons, Databases and Information Systems Commons, OS and Networks Commons, Science and Technology Studies Commons
Comments
Presented at the 2012 ACM Conference on Computer and Communications Security, Raleigh, NC, October 16-18, 2012.