Name
Abstract | ||
|---|---|---|
•We propose a flexible approach for de-identifying distributed biomedical datal.•Horizontal and vertical data distribution are handled in a consistent manner.•Our method supports a broad spectrum of anonymization methods and privacy criteria.•Supported algorithms include optimal methods, heuristics and clustering algorithms.•Applicable criteria include k-anonymity, l-diversity, t-closeness and d-presence. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.jbi.2013.12.002 | Journal of Biomedical Informatics |
Keywords | Field | DocType |
Personal data protection,Distribution,Privacy,Anonymization,Commutative encryption,Secure multi-party computing,SMC | Information system,Data mining,Organizational unit,Closeness,Computer science,Data anonymization,Encryption,Anonymity,Commutative encryption | Journal |
Volume | ISSN | Citations |
50 | 1532-0464 | 13 |
PageRank | References | Authors |
0.74 | 29 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Florian Kohlmayer | 1 | 53 | 6.97 |
| Fabian Praßer | 2 | 70 | 12.31 |
| Claudia Eckert | 3 | 76 | 13.13 |
| Klaus A. Kuhn | 4 | 568 | 142.21 |