Name
Abstract | ||
|---|---|---|
Today, people can use various database techniques to discover useful knowledge from large collections of data. However, people also face the risk of disclosing sensitive information to competitor when the data is shared between different organizations. Thus, there is a balance between the legitimate mining need and protection of confidential knowledge when people release or share data. In this paper, we study the privacy preserving in association rule mining. A new distortion-based method was proposed which hides sensitive rules by removing some items in database so as to reduce the support or confidence of sensitive rules below specified thresholds. Aimed at minimizing side effects, the number of sensitive rules and the number of non-sensitive rules supported by each transaction are utilized to sort the transactions and the candidates which contain most sensitive rules and least non-sensitive rules are selected to modify. Comparative experiments on real datasets showed that the new method can achieve satisfactory results with fewer side effects and data loss. © 2014 Springer International Publishing Switzerland. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-07455-9_9 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
Association rule hiding,privacy preserving data mining,sensitive association rules,side effects | Data mining,Heuristic,Data loss,Confidentiality,Computer science,sort,Association rule learning,Artificial intelligence,Information sensitivity,Database transaction,Distortion,Machine learning | Conference |
Volume | Issue | ISSN |
8481 LNAI | PART 1 | 0302-9743 |
Citations | PageRank | References |
2 | 0.37 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cheng Peng | 1 | 8 | 6.56 |
| Chu Shu-Chuan | 2 | 425 | 53.51 |
| Chun-Wei Lin | 3 | 1484 | 154.11 |
| John F. Roddick | 4 | 1908 | 331.20 |