Name
Abstract | ||
|---|---|---|
In recent years, the negative survey, which can preserve the privacy of individuals when used for collecting sensitive information, has attracted significant attention. However, the privacy of the typical negative survey is limited by the number of categories. When the number of categories is small, the typical negative survey exhibits weak privacy preservation. In particular, when only two categories exist, the typical negative survey cannot preserve the privacy of individuals. Moreover, at times, the privacy requirements of participants are strict. In such a situation, the typical negative survey fails to provide satisfactory privacy preservation. In this paper, two novel negative survey models that use negative combined categories (NCCs), NCC-I and NCC-II, are proposed. They can provide improved individual privacy preservation, in particular for sensitive information with only two categories. The experimental results demonstrate that the proposed methods can achieve superior privacy preservation and provide accurate reconstructed results. (C) 2020 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.asoc.2020.106578 | APPLIED SOFT COMPUTING |
Keywords | DocType | Volume |
Negative survey, Privacy protection, Sensitive information collection, Immune computation | Journal | 96 |
ISSN | Citations | PageRank |
1568-4946 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| hao jiang | 1 | 59 | 17.96 |
| Wenjian Luo | 2 | 356 | 40.95 |
| Binyao Duan | 3 | 0 | 0.68 |
| Chenwang Wu | 4 | 0 | 1.69 |