An Ensemble of Random Decision Trees with Personalized Privacy Preservation in Edge-Cloud Computing

Abstract

Edge-cloud computing (ECC) is an emerging computing paradigm, which offers a great opportunity to implement data mining-based services and applications for a large number of devices and sensors in Internet of Things. However, data privacy has been one of the most concerned problems for all the participants. In this paper, we propose a framework of privacy-preserving data mining based on private random decision trees in ECC. The framework not only gives the strong privacy guarantee, but also provides the efficient data utility. At first, we design a differential privacy-based framework to implement private random decision trees in ECC. Then, we present the concrete algorithms and the corresponding task that each participant needs to undertake. Next, we analyze the key factors to influence privacy and utility and give further improvement to increase the utility with strong privacy preservation. Finally, extensive experiments demonstrate the good performance of our designed framework.