Security of Random Data Perturbation Methods

Abstract

Statistical databases often use random data perturbation (RDP) methods to protect against disclosure of confidential numerical attributes. One of the key requirements of RDP methods is that they provide the appropriate level of security against snoopers who attempt to obtain information on confidential attributes through statistical inference. In this study, we evaluate the security provided by three methods of perturbation. The results of this study allow the database administrator to select the most effective RDP method that assures adequate protection against disclosure of confidential information.