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Presentation
Space Adaptation: Privacy-Preserving Multiparty Collaborative Mining with Geometric Perturbation
Proceedings of the Twenty-Sixth Annual ACM Symposium on Principles of Distributed Computing
  • Keke Chen, Wright State University - Main Campus
  • Ling Liu
Document Type
Conference Proceeding
Publication Date
8-1-2007
Abstract
The service-oriented infrastructure has become popular for collaboratively mining data distributed over organizations [3], where the participants are the data providers who submit their perturbed datasets to the designated data mining service provider (the data miner) for mining commonly interested models.
Comments

Presented at the 26th Annual ACM Symposium on Principles of Distributed Computing, Portland, OR, August 12-15, 2007.

DOI
10.1145/1281100.1281154
Citation Information
Keke Chen and Ling Liu. "Space Adaptation: Privacy-Preserving Multiparty Collaborative Mining with Geometric Perturbation" Proceedings of the Twenty-Sixth Annual ACM Symposium on Principles of Distributed Computing (2007) p. 324 - 325 ISSN: 9781595936165
Available at: http://works.bepress.com/keke_chen/24/