Name
Abstract | ||
|---|---|---|
Privacy-preserving data publication for data mining is to protect sensitive information of individuals in published data while the distortion to the data is minimized. Recently, it is shown that (fi;k)- anonymity is a feasible technique when we are given some sensitive at- tribute(s) and quasi-identifler attributes. In previous work, generalization of the given data table has been used for the anonymization. In this pa- per, we show that we can project the data onto two tables for publishing in such a way that the privacy protection for (fi;k)-anonymity can be achieved with less distortion. In the two tables, one table contains the undisturbed non-sensitive values and the other table contains the undis- turbed sensitive values. Privacy preservation is guaranteed by the lossy join property of the two tables. We show by experiments that the results are better than previous approaches. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-72524-4_75 | APWeb/WAIM |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
6 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raymond Chi-Wing Wong | 1 | 1305 | 85.98 |
| Yu-Bao Liu | 2 | 164 | 16.36 |
| Jian Yin | 3 | 105 | 6.71 |
| Zhilan Huang | 4 | 3 | 1.40 |
| Ada Wai-Chee Fu | 5 | 4646 | 417.59 |
| Jian Pei | 6 | 19002 | 995.54 |