Presentation
Space Adaptation: Privacy-Preserving Multiparty Collaborative Mining with Geometric Perturbation
Proceedings of the Twenty-Sixth Annual ACM Symposium on Principles of Distributed Computing
Document Type
Conference Proceeding
Publication Date
8-1-2007
Disciplines
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.
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/
Presented at the 26th Annual ACM Symposium on Principles of Distributed Computing, Portland, OR, August 12-15, 2007.