The Internet of Things has allowed for the development of numerous consumer-facing apps and services that enhance our knowledge of and ability to shape our built environments and the quality of our individual lives. These services couldn't exist without the persistent gathering and analysing of private and personal information about users. When it comes to protecting users from identification, profiling, localization and tracking, and information linking, smart heath care is one of the many IoT applications that requires privacy preservation strategies. Finding the right balance between privacy protection, data utility, and acceptable system performance in terms of accuracy, runtime, and resource consumption requires carefully selecting privacy preservation techniques (and solutions) based on the nature of data, system performance requirements, and resource constraints. In this study, we evaluate the effects of introducing our preferred privacy preservation techniques on the functionality of various nodes in the IoT ecosystem, both in terms of data utility and overall system performance. Using both real-world and synthetic privacy-preserving smart health care datasets, we build, illustrate, and assess the results of our proposed methodologies. We begin with a comprehensive taxonomy and analysis of privacy preservation strategies and solutions that can be used as a starting point for making informed decisions about which methods to employ given the specifics of a given data set and the constraints of a given system. Furthermore, we discuss and implement a strategy for constructing realistic synthetic and privacy-preserving smart health care datasets utilising Generative Adversarial Networks and Differential Privacy to promote privacy-preserving data exchange. We utilise healthcare data as an example later on to describe and design a solution for private data analytics: the differential privacy library PyDPLib. We present and implement a novel approach to reconfigurable data privacy in machine learning on resource-limited computing devices, complete with corresponding algorithms and an end-to-end system pipeline. This allows us to find appropriate trade-offs between providing the necessary privacy preservation, device resource consumption, and application accuracy.

IoT Eco system, GAN