LAUSR

Massive Internet of Things (IoT) data sets are possessed by big institutions serving daily life because IoT devices are widely used in our daily life such as wearable devices and smart home devices. Publishing these data sets among various institutions causes an increasing number of users to concern their personal privacy. Differential privacy is the state-of-the-art concept of privacy preservation, but it suffers from the low accuracy. In this article, we improve differentially private mechanisms including the Laplace mechanism as well as the sample and aggregation mechanism by bringing the personalized sampling technology into these mechanisms so that IoT data sets can be privately published through differentially private mechanisms. In particular, improved mechanisms assign a personalized sampling probability to each data record in a way that their accuracy can be improved. We analyse improved mechanisms in terms of their privacy and accuracy. Then, we empirically demonstrate that the performance of improved mechanisms is better than original mechanisms through extensive experiments on synthetic data sets and real-world data sets.