Name
Abstract | ||
|---|---|---|
The goal of statistical disclosure control (SDC) is to modify statistical data so that it can be published without releasing confidential information that may be linked to specific respondents. The challenge for SDC is to achieve this variation with minimum loss of the detail and accuracy sought by final users. There are many approaches to evaluate the quality of a protection method. However, all these measures are only applicable to numerical or categorical attributes. In this paper, we present some recent results about time series protection and re-identification. We propose a complete framework to evaluate time series protection methods. We also present some empirical results to show how our framework works. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.ins.2009.01.024 | Inf. Sci. |
Keywords | Field | DocType |
empirical result,framework work,protection method,categorical attribute,statistical data,complete framework,time series protection,confidential information,statistical disclosure control,time series protection method,time series | Confidentiality,Computer science,Categorical variable,Artificial intelligence,Statistical disclosure control,Machine learning | Journal |
Volume | Issue | ISSN |
179 | 11 | 0020-0255 |
Citations | PageRank | References |
15 | 0.68 | 29 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jordi Nin | 1 | 311 | 26.53 |
| Vicenç Torra | 2 | 2666 | 234.27 |