Preventing Interval-based Inference by Random Data Perturbation

• Main Authors: LI, Yingjiu, WANG, Lingyu, Jajodia, Sushil
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2002
• Subjects: Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/1049; http://dx.doi.org/10.1007/1-4020-8128-6_6
• Institution: Singapore Management University

Random data perturbation (RDP) method is often used in statistical databases to prevent inference of sensitive information about individuals from legitimate sum queries. In this paper, we study the RDP method for preventing an important type of inference: interval-based inference. In terms of interval-based inference, the sensitive information about individuals is said to be compromised if an accurate enough interval, called inference interval, is obtained into which the value of the sensitive information must fall. We show that the RDP methods proposed in the literature are not effective for preventing such interval-based inference. Based on a new type of random distribution, called Ɛ-Gaussian distribution, we propose a new RDP method to guarantee no interval-based inference.