Name
Abstract | ||
|---|---|---|
Data anonymization is the process of de-identifying sensitive data while preserving its format and data type. The masked data can be a realistic or a random sequence of data, dependent on the technique used for anonymization. Individual privacy can be at risk if a published data set is not properly de-identified. The most known approach of anonymization is k-anonymity that can be viewed as clustering with a constraint of k minimum objects in every cluster. In this paper, we propose a new anonymization approach based on multi-view topological collaborative clustering. The proposed method has the advantage of detecting the k level automatically. The aim of collaborative clustering is to reveal the common structure of data using different views on variables, it allows to take into account other knowledges without recourse to the data in an unsupervised learning frame. The proposed approach has been validated on several data sets, and experimental results have shown very promising performance. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-01424-7_79 | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT III |
Keywords | Field | DocType |
Anonymization, Collaborative clustering, Multi-view | Topology,Data set,Computer science,Random sequence,Data anonymization,k-anonymity,Unsupervised learning,Data type,Cluster analysis | Conference |
Volume | ISSN | Citations |
11141 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sarah Zouinina | 1 | 0 | 1.35 |
| Nistor Grozavu | 2 | 67 | 16.76 |
| Younès Bennani | 3 | 269 | 53.18 |
| Abdelouahid Lyhyaoui | 4 | 52 | 6.88 |
| Nicoleta Rogovschi | 5 | 40 | 8.42 |