期刊名称：International Journal on Computer Science and Engineering
印刷版ISSN：2229-5631
电子版ISSN：0975-3397
出版年度：2010
卷号：2
期号：3
页码：860-864
出版社：Engg Journals Publications
摘要：Data mining is a powerful tool which supports automatic extraction of unknown patterns from large amounts of data. The knowledge extracted by data mining process support a variety of domains like marketing, weather forecasting, and medical diagnosis .The process of data mining requires a large data to be collected from diverse sites. With the rapid growth of the Internet, networking, hardware and software technology there is tremendous growth in the amount of data collection and data sharing. Huge volumes of detailed data are regularly collected from organizations and such datasets also contain personal as well as sensitive data about individuals. Though the data mining operation extracts useful knowledge to support variety of domains but access to personal data poses a threat to individual privacy. There is increased concern on how sensitive and private information can be protected while performing data mining operation. Privacy preserving data mining algorithms gives solution for the privacy problem. PPDM gives valid data mining results and also guarantees privacy protection for sensitive data stored in the data warehouse. In this paper we analyzed the threats to privacy that can occur due to data mining process. We have proposed a framework that allows systemic transformation of original data using randomized data perturbation technique and the modified data is submitted as a result of query to the parties using decision tree approach. This approach gives the valid results for analysis purpose but the actual or true data is not revealed and the privacy is preserved.
关键词：Data perturbation; Data mining; Decision tree; Privacy preservation; Sensitive data.
