The Heinz School
Carnegie Mellon University
Hiding data values in privacy-preserving data mining (PPDM) protects information against unauthorized attacks while maintaining analytical data properties. The most popular models are designed for constant data environments. They are usually computationally expensive for large data sizes and have poor real-time performance on frequent data growth. Considering that updates and growth of source data are becoming more and more popular in online environments, a PPDM model that has quick responses on the data updates in real-time is appealing. To increase the speed and response of the singular value decomposition (SVD) based model, we have applied an improved incremental SVD-updating algorithm. The performance and effectiveness of the improved algorithm have been examined on synthetic and real data sets. Experimental results indicate that the introduction of the incremental matrix decomposition produces a significant increase in speed for the SVD-based data value hiding method, better scalability, and better real-time performance of the model, thereafter. It also provides potential support for the use of the SVD technique in the On-Line Analytical Processing for business data analysis.
Mathematics Subject Classification:
The research work of Jun Zhang was supported in part by the U.S. National Science Foundation under grant CCF-0527967, in part by the National Institutes of Health under grant 1R01HL086644-01, in part by the Kentucky Science and Engineering Foundation under grant KSEF-148-502-06-186, and in part by the Alzheimer's Association under Grant NIGR-06-25460.