Department of Computer Science
Graph-based abstraction for privacy preserving manifold visualization
With the next-generation Web aiming to further facilitate data/information sharing and aggregation, providing data privacy protection support in an open networked environments becomes increasingly important. Learning-from abstraction is a recently proposed distributed data mining approach which first abstracts data at local sources using the agglomerative hierarchical clustering (AGH) algorithm and then aggregates the abstractions (instead of the data) for global analysis. In this paper, we explain the limitation of the use of AGH for local manifold preserving data abstraction and propose the use of the graph-based clustering approach (e.g., the minimum cut) for local data abstraction. The effectiveness of the proposed abstraction approach was evaluated using benchmarking datasets with promising results. The global analysis results obtained based on the minimum cut abstraction was found to outperform those based on the AGH abstraction, especially when the underlying manifold was complex.
Data visualization, Next generation networking, Data privacy, Protection, Data mining, Abstracts, Clustering algorithms, Aggregates, Data analysis, Algorithm design and analysis
Source Publication Title
Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops (WI-IAT 2006 Workshops)(WI-IATW'06)
Hong Kong, China
Copyright © 2006 by The Institute of Electrical and Electronics Engineers, Inc.
This work is supported by HKBU FRG/05-06/I-6 and RGC HKBU/2102/06E.
Link to Publisher's Edition
Zhang, X., Cheung, W., & Li, C. (2006). Graph-based abstraction for privacy preserving manifold visualization. Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops (WI-IAT 2006 Workshops)(WI-IATW'06), 94-97. https://doi.org/10.1109/WI-IATW.2006.76