Bibliographic Details
| Title: |
Anonymizing transactional datasets. |
| Authors: |
AL Bouna, Bechara, Clifton, Chris1, Malluhi, Qutaibah2 |
| Source: |
Journal of Computer Security. Jan2015, Vol. 23 Issue 1, p89-106. 18p. |
| Subjects: |
Data privacy, Computer security research, Big data, Data protection research, Mathematical decomposition |
| 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. [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
