High utility differential privacy based on smooth sensitivity and individual ranking

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.