| Autor: |
Yan, Yan, Eyeleko, Anselme Herman, Mahmood, Adnan, Sun, Zichao, Dong, Zhuoyue, Xu, Fei |
| Zdroj: |
International Journal of Security and Networks; 2023, Vol. 18 Issue: 2 p75-90, 16p |
| Abstrakt: |
The publishing and using of big data brought unprecedented convenience to users. However, it also results in the disclosure of personal privacy information. In order to mitigate the privacy leakage risk of sensitive information during dynamic data updating, this paper envisages a non-synonymous diverse anatomy method for the privacy preserving dynamic data publishing. The envisaged method inherits the advantages of the traditional anatomy method, retains the availability of the original data to the greatest extent, and avoids the loss of information caused by the generalisation process. A series of indicators are designed to evaluate the synonymous linkage between non-numerical sensitive values. A novel grouping mechanism is further proposed to achieve l-diversity anatomy by combining the concept of synonymous linkage and synonymous entropy with the dynamic update procedure. Experimental analysis suggests that the envisaged method can provide better privacy protection effect on the published data. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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