Research on Privacy-Preserving Technology for Cloud Computing

DOI

10.2991/icacsei.2013.152How to use a DOI?

Keywords

Cloud computing, Security risks, SVM, Optimized PPSVM, Secure Multi-Party Computation. I. Introduction

Abstract

Consumers will be able to access applications and data anywhere in the world on demand by cloud computing which promises reliable services delivered through next-generation data centers. Cloud computing has a very broad application prospects such as virtualization, large-scale, dynamic configuration and many other characters. At the same time, there are many security risks such as privacy information leakage in the network by the rapid growing of network security threats. Security issues is the key issues constraining the development of cloud computing. The Privacy Protection Support Vector Machine (PPSVM) is widely concerned in secure multi-party computation (SMC). We propose a new optimized Privacy Protection Support Vector Machine classifier without Secure Multi-Party Computation for vertically partitioned data set which is not disclosing the private data. The novel approach is proved as being greater than traditional classification SVM on privacy-preserving by some experiments.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiao long Wang
PY  - 2013/08
DA  - 2013/08
TI  - Research on Privacy-Preserving Technology for Cloud Computing
BT  - Proceedings of the 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013)
PB  - Atlantis Press
SP  - 636
EP  - 639
SN  - 1951-6851
UR  - https://doi.org/10.2991/icacsei.2013.152
DO  - 10.2991/icacsei.2013.152
ID  - Wang2013/08
ER  -