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Differentially Private Tree-Based Contextual Online Learning for Service Big Data Selection in IoT

With the rapidly growing number of connected smart devices deployed and diverse services provided in the Internet of Things (IoT), selecting proper services for users is becoming more and more important. However, challenges exist as a result of the highly heterogeneous environments, characteristics of various kinds of users and the myriad services offered by many service providers, which have promising applications in the IoT era. In the meantime, users' contexts (e.g., location, time, and surroundings) are wildly utilized in the IoT scenario to better satisfy individuals' demands, raising privacy issues among people. To address these problems, we proposed a differentially private tree-based contextual online learning approach for IoT service selection to select suitable services for users. Leveraging on the historical records of services' and users' feedback, our algorithm achieves high prediction accuracy. Besides, instead of considering the services as individual items, we utilize a top-down cover tree structure to select services, which supports increasing large-scale dataset and diverse natural conditions. We theoretically prove that the accumulative regret of our approach has a sublinear bound and our experiment confirms that it can handle big data problems while achieving a balance between privacy-preserving level and service selection accuracy.