Name
Abstract | ||
|---|---|---|
Anonymization methods are an important tool to protect privacy. The goal is to release data while preventing individuals from being identified. Most approaches generalize data, reducing the level of detail so that many individuals appear the same. An alternate class of methods, including anatomy, fragmentation, and slicing, preserves detail by generalizing only the link between identifying and sensitive data. We investigate learning association rules on such a database. Association rule mining on a generalized database is challenging, as specific values are replaced with generalizations, eliminating interesting fine-grained correlations. We instead learn association rules from a fragmented database, preserving fine - grained values. Only rules involving both identifying and sensitive information are affected; we demonstrate the efficacy of learning in such environment. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-17016-9_23 | DATA PRIVACY MANAGEMENT, AUTONOMOUS SPONTANEOUS SECURITY, AND SECURITY ASSURANCE |
Keywords | Field | DocType |
Anonymity, Fragmentation, Database, Association rule mining, Data privacy | Data mining,Level of detail,Generalization,Computer science,Association rule learning,Anonymity,Information privacy,Information sensitivity,Database | Conference |
Volume | ISSN | Citations |
8872 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amel Hamzaoui | 1 | 0 | 0.34 |
| Qutaibah M. Malluhi | 2 | 189 | 55.68 |
| Chris Clifton | 3 | 3327 | 544.44 |
| Ryan Riley | 4 | 348 | 19.75 |