Data Level Privacy Preserving: A Stochastic Perturbation Approach Based on Differential Privacy (Extended abstract)

Chuan Ma, Long Yuan, Li Han, Ming Ding, Raghav Bhaskar, Jun Li

Abstract: With the great amount of available data, especially collected from the ubiquitous Internet of Things (IoT), the issue of privacy leakage has been an increasing concern recently. To preserve the privacy of IoT datasets, traditional methods usually calibrate random noises on the data values to achieve differential privacy (DP) [1]. However, the amount of calibrating noises should be carefully designed and a heedless value will definitely degrade the availability of datasets.