Some ideas on privacy-aware data analytics in the internet-of-everything

In this chapter, we discuss some issues concerning the computation of machine learning models for data analytics on the Internet-of-Everything. We model such computations as compositions of services that form a process whose main stages are acquisition, preparation, model training, and model-based inference. Then, we discuss randomiza-tion-as-a-service as a key technique for limiting undesired information disclosure during this process. We recall some fundamental results showing that randomization decreases the severity of disclosure, but at the same time has an adverse effect on data utility, in our case the data business value within the specific IoE application. We argue that non-interactive randomization at data acquisition time, while decreasing utility, can provide maximum flexibility and best accommodate provisions for compliance with regulations, ethics and cultural factors.