Name
Abstract | ||
|---|---|---|
Due to substantial commercial benefits in the discovered frequent patterns from large databases, frequent itemsets mining has become one of the most meaningful studies in data mining. However, it also increases the risk of disclosing some sensitive patterns through the data mining process. In this paper, a multi-objective integer programming, considering both data accuracy and information loss, is proposed to solve the problem for hiding sensitive frequent itemsets. Further, we solve this optimization model by a two-phased procedure, where in the first procedure the sanitized transactions can be pinpointed and in the second procedure the sanitized items can be pinpointed. Finally, we conduct some extensive tests on publicly available real data. These experiments' results illustrate that our approach is very effective. © 2013 Springer-Verlag Berlin Heidelberg. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-39787-5-27 | KSEM |
Keywords | Field | DocType |
association rule,multi-objective integer programming,privacy preserving data mining,sensitive knowledge protection | Data accuracy,Data mining,Information loss,Computer science,Integer programming,Association rule learning,Artificial intelligence,Machine learning | Conference |
Volume | Issue | ISSN |
8041 LNAI | null | 16113349 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingzheng Wang | 1 | 251 | 15.78 |
| Yue He | 2 | 105 | 16.62 |
| Donghua Pan | 3 | 23 | 5.53 |