Differential Privacy Techniques for Real-Time Analytics on IoT Data Streams

The proliferation of Internet of Things technologies has led to the constant processing of data through the available Internet of Things technology, resulting in the application of real-time data analytics in decision-making processes. Although real-time IoT data analytics applications are highly useful in terms of functionality, they also have severe implications regarding the privacy of the gathered data based on the nature of the gathered information. Currently available approaches toward the application of data privacy in their traditional roles are not very effective in this dynamic environment, especially given the constant nature of the data streams along with lower latency requirements in terms of times involved in processing the gathered data. Differential privacy has proven itself as a validated mathematical model in this field, capable in terms of meeting high requirements of data privacy in addition to facilitating the process of data analysis. This review article proposed here intends to develop a complete review process of methods taking place in differential privacy applied in real-time data analysis in IoT data streams, gathering information on the complete subject matter through research collaboration.