A Close Look at Privacy Preserving Data Mining Methods

• Main Authors: WU, Xindong, WANG, Yunfeng, CHU, Chao-Hsien, LIU, Fengli, CHEN, Ping, YUE, Dianmin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2006
• Subjects: Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/602; https://aisel.aisnet.org/pacis2006/32/
• Institution: Singapore Management University

Recent advances in information, communications, data mining, and security technologies have gave rise to a new era of research, known as privacy preserving data mining (PPDM). Several data mining algorithms, incorporating privacy preserving mechanisms, have been developed that allow one to extract relevant knowledge from large amount of data, while hide sensitive data or information from disclosure or inference. PPDM is a new attempt; thus, several research questions have often being asked. For instance: (1) how to measure the performance of these algorithms? (2) how effective of these algorithms in terms of privacy preserving? (3) will they impact the accuracy of data mining results? And (4) which one can better protect sensitive information? To help answer these questions, we conduct an extensive review on literature. We present a classification scheme, adopted from early studies, to guide the review process. Finally, we share directions for future research.