Name
Abstract | ||
|---|---|---|
In this paper, we study the privacy breach caused by unsafe correlations in transactional data where individuals have multiple tuples in a dataset. We provide two safety constraints to guarantee safe correlation of the data: 1 the safe grouping constraint to ensure that quasi-identifier and sensitive partitions are bounded by l-diversity and 2 the schema decomposition constraint to eliminate non-arbitrary correlations between non-sensitive and sensitive values to protect privacy and at the same time increase the aggregate analysis. In our technique, values are grouped together in unique partitions that enforce l-diversity at the level of individuals. We also propose an association preserving technique to increase the ability to learn/analyze from the anonymized data. To evaluate our approach, we conduct a set of experiments to determine the privacy breach and investigate the anonymization cost of safe grouping and preserving associations. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.3233/JCS-140517 | Journal of Computer Security |
Keywords | Field | DocType |
transactional data,data privacy | Data mining,Tuple,Computer science,Data anonymization,Correlation,Information privacy,Schema (psychology),Transactional leadership,Transaction data,Bounded function | Journal |
Volume | Issue | ISSN |
23 | 1 | 0926-227X |
Citations | PageRank | References |
3 | 0.44 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Béchara Al Bouna | 1 | 45 | 11.20 |
| Chris Clifton | 2 | 3327 | 544.44 |
| Qutaibah Malluhi | 3 | 217 | 10.17 |