A family of enhanced (L,alpha) diversity models for privacy preserving data publishing
Article
| Article Title | A family of enhanced (L,alpha) diversity models for privacy preserving data publishing |
|---|---|
| ERA Journal ID | 17858 |
| Article Category | Article |
| Authors | Sun, Xiaoxun (Author), Li, Min (Author) and Wang, Hua (Author) |
| Journal Title | Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications |
| Journal Citation | 27 (3), pp. 348-356 |
| Number of Pages | 9 |
| Year | 2011 |
| Publisher | Elsevier |
| Place of Publication | Netherlands |
| ISSN | 0167-739X |
| 1872-7115 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.future.2010.07.007 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0167739X1000141X |
| Abstract | Privacy preservation is an important issue in the release of data for mining purposes. Recently, a novel l-diversity privacy model was proposed. However, even an l-diverse data set may have some severe problems leading to the revelation of individual sensitive information. In this paper, we remedy the problem by introducing distinct (l,α)-diversity, which, intuitively, demands that the total weight of the sensitive values in a given QI-group is at least α, where the weight is controlled by a pre-defined recursive metric system. We provide a thorough analysis of the distinct (l,α)-diversity and prove that the optimal distinct (l,α)-diversity problem with its two variants entropy (l,α)-diversity and recursive (c,l,α)-diversity are NP-hard, and propose a top-down anonymization approach to solve the distinct (l,α)-diversity problem with its variants. We show in the extensive experimental evaluations that the proposed methods are practical in terms of utility measurements and can be implemented efficiently. |
| Keywords | anonymization; data publishing; data sets; experimental evaluation; NP-hard; privacy models; privacy preservation; sensitive informations; topdown |
| ANZSRC Field of Research 2020 | 460401. Cryptography |
| 460499. Cybersecurity and privacy not elsewhere classified | |
| 460905. Information systems development methodologies and practice | |
| Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
| Byline Affiliations | Department of Mathematics and Computing |
https://research.usq.edu.au/item/9zzqz/a-family-of-enhanced-l-alpha-diversity-models-for-privacy-preserving-data-publishing
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