Modeling Identity Disclosure Risk Estimation Using Kenyan Situation

Identity disclosure risk is an essential consideration in data anonymization aimed at preserving privacy and utility. The risk is regionally dependent. Therefore, there is a need for a regional empirical approach in addition to a theoretical approach in modeling disclosure risk estimation. Reviewed literature pointed to three influencers of the risk. However, we did not find literature on the combined effects of the three influencers and their predictive power. To fill the gap, this study modeled the risk estimation predicated on the combined effect of the three predictors using the Kenyan situation. The study validated the model by conducting an actual re-identification quasi-experiment. The adversary's analytical competence, distinguishing power of the anonymized datasets, and linkage mapping of the identified datasets are presented as the predictors of the risk estimation. For each predictor, manifest variables are presented. Our presented model extends previous models and is capable of producing a realistic risk estimation.