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Presentation
PerturBoost: Practical Confidential Classifier Learning in the Cloud
Proceedings of the 13th IEEE International Conference on Data Mining
  • Keke Chen, Wright State University - Main Campus
  • Shumin Guo, Wright State University - Main Campus
Document Type
Conference Proceeding
Publication Date
12-1-2013
Abstract

Mining large data requires intensive computing resources and data mining expertise, which might not be available for many users. With the development of cloud computing and services computing, data mining tasks can now be moved to the cloud or outsourced to third parties to save costs. In this new paradigm, data and model confidentiality becomes the major concern to the data owner. Meanwhile, users are also concerned about the potential tradeoff among costs, model quality, and confidentiality. In this paper, we propose the PerturBoost framework to address the problems in confidential cloud or outsourced learning. PerturBoost combined with the random space perturbation (RASP) method that was also developed by us can effectively protect data confidentiality, model confidentiality, and model quality with low client-side costs. Based on the boosting framework, we develop a number of base learner algorithms that can learn linear classifiers from the RASP-perturbed data. This approach has been evaluated with public datasets. The result shows that the RASP-based PerturBoost can provide model accuracy very close to the classifiers trained with the original data and the AdaBoost method, with high confidentiality guarantee and acceptable costs.

Comments

Presented at the IEEE 13th International Conference on Data Mining, Dallas, TX, December 7-10, 2013.

DOI
10.1109/ICDM.2013.118
Citation Information
Keke Chen and Shumin Guo. "PerturBoost: Practical Confidential Classifier Learning in the Cloud" Proceedings of the 13th IEEE International Conference on Data Mining (2013) p. 991 - 996 ISSN: 15504786
Available at: http://works.bepress.com/keke_chen/45/