Using safety constraint for transactional dataset anonymization
| Author | Al Bouna, Bechara |
| Author | Clifton, Chris |
| Author | Malluhi, Qutaibah |
| Available date | 2024-07-17T07:14:53Z |
| Publication Date | 2013 |
| Publication Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Resource | Scopus |
| Identifier | http://dx.doi.org/10.1007/978-3-642-39256-6_11 |
| ISSN | 3029743 |
| Abstract | In this paper, we address privacy breaches in transactional data where individuals have multiple tuples in a dataset. We provide a safe grouping principle to ensure that correlated values are grouped together in unique partitions that enforce l-diversity at the level of individuals. We conduct a set of experiments to evaluate privacy breach and the anonymization cost of safe grouping. |
| Language | en |
| Publisher | Springer |
| Subject | Anonymization L diversities Privacy breaches Safety constraint Transactional data Artificial intelligence Computer science |
| Type | Conference Paper |
| Pagination | 164-178 |
| Volume Number | 7964 LNCS |
Files in this item
| Files | Size | Format | View |
|---|---|---|---|
|
There are no files associated with this item. |
|||
This item appears in the following Collection(s)
-
Computer Science & Engineering [2262 items ]