Towards Real-time Performance of Data Value Hiding for Frequent Data Updates

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 computationallyexpensive 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 in

• Type: Conference
• Year: 2008
• Where: GRC
• Authors: Jie Wang, Justin Zhan, Jun Zhang
• Keywords: Analytical Data Properties, Applied Computing, Constant Data Environments, GRC 2008, Privacy-preserving Data Mining