Privacy-Preserving Data Mining through Knowledge Model Sharing

Privacy-preserving data mining (PPDM) is an important topic to both industry and academia. In general there are two approaches to tackling PPDM, one is statistics-based and the other is crypto-based. The statistics-based approach has the advantage of being efficient enough to deal with large volume of datasets. The basic idea underlying this approach is to let the data owners publish some sanitized versions of their data (e.g., via perturbation, generalization, or -diversification), which are then used for extracting useful knowledge models such as decision trees. In this paper, we present a new method for statistics-based PPDM. Our method differs from the existing ones because it lets the data owners share with each other the knowledge models extracted from their own private datasets, rather than to let the data owners publish any of their own private datasets (not even in any sanitized form). The knowledge models derived from the individual datasets are used to generate some pseudo-d...

• Type: Conference
• Year: 2007
• Where: KDD
• Authors: Patrick Sharkey, Hongwei Tian, Weining Zhang, Shouhuai Xu
• Keywords: Data Mining, KDD 2007, Knowledge Models, Private Datasets, Useful Knowledge Models