Evaluating Re-Identification Risks of Data Protected by Additive Data Perturbation

Commercial organizations and government agencies that gather, store, share and disseminate data are facing increasing concerns over individual privacy and confidentiality. Confidential data is often masked in the database or prior to release to a third party, through methods such as data perturbation. In this study, re-identification risks of three major additive data perturbation techniques were compared using two different record linkage techniques. The results suggest that re-identification risk of Kim's multivariate noise addition method is similar to that of simple noise addition method. The general additive perturbation method (GADP) has the lowest re-identification risk and therefore provides the highest level of protection. The study also suggests that Fuller's method of assessing re-identification risk may be better suited than the probabilistic record-linkage method of Winkler, for numeric data. The results of this study should be help organizations and government agencies choose an appropriate additive perturbation technique.