Name
Abstract | ||
|---|---|---|
Privacy preserving data mining (PPDM) has become a popular topic in the research community. How to strike a balance between privacy protection and knowledge discovery in the sharing process is an important issue. This study focuses on privacy preserving utility mining (PPUM) and presents two novel algorithms, HHUIF and MSICF, to achieve the goal of hiding sensitive itemsets so that the adversaries can not mine them from the modified database. In addition, we minimize the impact on the sanitized database in the process of hiding sensitive itemsets. The experimental results show that HHUIF achieves the lower miss costs than MSICF does on two synthetic datasets. On the other hand, MSICF generally has the lower difference between the original and sanitized databases than HHUIF does. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ISDA.2008.89 | ISDA (1) |
Keywords | Field | DocType |
novel algorithm,privacy preserving utility mining,sensitive itemsets,sharing process,important issue,modified database,novel algorithms,lower difference,data mining,knowledge discovery,privacy protection,privacy preserving data mining,security of data,hafnium,data privacy,databases,privacy,association rules | Data mining,Utility mining,Computer science,Algorithm,Association rule learning,Knowledge extraction,Information privacy | Conference |
Volume | ISBN | Citations |
1 | 978-0-7695-3382-7 | 4 |
PageRank | References | Authors |
0.41 | 17 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jieh-Shan Yeh | 1 | 230 | 12.38 |
| Po-Chiang Hsu | 2 | 67 | 3.48 |
| Ming-Hsun Wen | 3 | 4 | 0.41 |