Data Level Privacy Preserving: A Stochastic Perturbation Approach Based on Differential Privacy

With the great amount of available data, especially collecting from the ubiquitous Internet of Things (IoT), the issue of privacy leakage arises increasingly concerns recently. To preserve the privacy of IoT datasets, traditional methods usually calibrate random noises on the data values to achieve differential privacy (DP). However, the amount of the calibrating noises should be carefully designed and a heedless value will definitely degrade the availability of datasets. Thus, in this work, we propose a stochastic perturbation method to sanitize the dataset, where the perturbation is obtained from the rest samples in the same dataset. In addition, we derive the expression of the utility level based on its unique framework and prove that the proposed algorithm can achieve the $\epsilon$e-DP. To show the effectiveness of the proposed algorithm, we conduct extensive experiments on real-life datasets by various functions, such as query answers and machine learning tasks. By comparing with the state-of-the-art methods, our proposed algorithm can achieve a better performance under the same privacy level.