Name
Abstract | ||
|---|---|---|
Synthetic data generation has been proposed as a flexible alternative to more traditional statistical disclosure control (SDC) methods for limiting disclosure risk. Synthetic data generation is functionally distinct from standard SDC methods in that it breaks the link between the data subjects and the data such that reidentification is no longer meaningful. Therefore orthodox measures of disclosure risk assessment - which are based on reidentification - are not applicable. Research into developing disclosure assessment measures specifically for synthetic data has been relatively limited. In this paper, we develop a method called Differential Correct Attribution Probability (DCAP). Using DCAP, we explore the effect of multiple imputation on the disclosure risk of synthetic data. |
Year | Venue | Field |
|---|---|---|
2018 | PSD | Synthetic data generation,Data mining,Computer science,Risk assessment,Synthetic data,Attribution,Imputation (statistics),Limiting,Statistical disclosure control |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jennifer Taub | 1 | 0 | 0.34 |
| Mark Elliot | 2 | 27 | 6.15 |
| Maria Pampaka | 3 | 10 | 4.20 |
| Duncan Smith | 4 | 145 | 11.74 |