Authors: Fagen Song; Tinghuai Ma
Addresses: Yancheng Institute of Technology, University in Yancheng, Yancheng, Jiangsu, China ' Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
Abstract: Differential privacy can provide provable privacy security protection. In recent years, a great improvement has been made, however, in practical applications, the utility of original data is highly susceptible to noise, and thus, it limits its application and extension. To address the above problem, a new differential privacy method based on smooth sensitivity has been proposed in this paper. Using this method, the dataset's utility is improved greatly by reducing the amount of noise that is added, and this was validated by experiments.
Keywords: differential privacy; privacy protection; data publish; smooth sensitivity; k-anonymous; information security; computer security; data publishing; high utility; individual ranking.
International Journal of Information and Computer Security, 2021 Vol.15 No.2/3, pp.216 - 230
Received: 30 Sep 2017
Accepted: 29 May 2018
Published online: 09 Jul 2021 *