Name
Abstract | ||
|---|---|---|
Concept hierarchies are widely used in multiple fields to carry out data analysis. In data privacy, they are known as Value Generalization Hierarchies (VGHs), and are used by generalization algorithms to dictate the data anonymization. Thus, their proper specification is critical to obtain anonymized data of good quality. The creation and evaluation of VGHs require expert knowledge and a significant amount of manual effort, making these tasks highly error-prone and time-consuming. In this paper we present AIKA, a knowledge-based framework to automatically construct and evaluate VGHs for the anonymization of categorical data. AIKA integrates ontologies to objectively create and evaluate VGHs. It also implements a multi-dimensional reward function to tailor the VGH evaluation to different use cases. Our experiments show that AIKA improved the creation of VGHs by generating VGHs of good quality in less time than when manually done. Results also showed how the reward function properly captures the desired VGH properties. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-47650-6_21 | Lecture Notes in Artificial Intelligence |
Field | DocType | Volume |
Semantic similarity,Ontology (information science),Data mining,Use case,Categorical variable,Computer science,Data anonymization,Publishing,Hierarchy,Information privacy | Conference | 9983 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vanessa Ayala-Rivera | 1 | 18 | 3.96 |
| Liam Murphy | 2 | 811 | 74.94 |
| Christina Thorpe | 3 | 53 | 9.00 |