| Abstrakt: |
The amount of data created by individuals increases daily. These data may be gathered from various sources, such as social networks, e-commerce websites and healthcare systems, and they are frequently made available to third-party research and commercial organisations to facilitate a wide range of data studies. The protection of sensitive and confidential information included within the datasets to be published must be addressed, even though publishing data can assist organisations in improving their service offerings and developing new solutions that would not otherwise be available. The research community has invested great effort over the past two decades to comprehend how individuals' privacy may be preserved when their data need to be published. Disassociation is a common approach for anonymising transactional data against re-identification attacks in privacy-preserving data publishing. To address this issue, we proposed three new strategies for horizontal partitioning: suppression, adding and remaining list. Each strategy identifies a different approach for handling small clusters with fewer than k transactions. We used three real datasets for transactional data in our experiments, and our findings showed that our proposed strategies could decrease the percentage of information loss of disassociated transactional data by almost 35%, comparing it with the previous original disassociation algorithm. As a result, the utility of published data will be improved. [ABSTRACT FROM AUTHOR] |
