An Authentication Method Based on the Turtle Shell Algorithm for Privacy-Preserving Data Mining
Rong Wang, Yan Zhu, Tung-Shou Chen, Chin‐Chen Chang · The Computer Journal · 2018
Outsourcing data mining tasks is beneficial for data owners who either lack expertise in data mining or sufficient computing resources. However, directly releasing the original data would leak private information. Research on Privacy-Preserving Data Mining (PPDM) is dedicated to addressing this issue, the aim of this research is to reduce the risk of privacy violations and preserve the knowledge in the original data. However, most existing methods in the literature ignore the case in which service providers want to verify the integrity and authenticity of their clients’ data to avoid data tampering before performing data mining tasks. In this paper, a new method is proposed to extend the turtle shell algorithm of data hiding to protect the privacy of the original data and to acquire authentication functions simultaneously. The act of data perturbation is performed by replacing data values with their closest neighbors according to a reference matrix. Further, a message authentication code is hidden in the perturbed data to verify the integrity and authenticity of the perturbed data. The experimental results showed that the proposed method achieved the purpose of data perturbation and outperformed similar methods in satisfying the PPDM requirement.