A Review on Machine Learning and Deep Learning Models for Privacy Preservation in IOT and IOMT Applications

The concept of Internet of Multimedia Things (IoMT) is becoming popular now a days and can be used in various smart city applications, e.g., traffic management, healthcare, and surveillance. In the IoMT, devices, e.g., Multimedia Sensor Nodes (MSNs) are capable of generating both multimedia and non-multimedia data. The generated data are forwarded to a cloud server via a Base Station (BS). However, it is possible that the Internet connection between the BS and cloud server may be temporarily down. The limited computational resources restrict the MSNs from holding the captured data for a longer time. In this situation, mobile sinks can be utilized to collect data from MSNs and upload to the cloud server. However, this data collection may create privacy issues, e.g., revealing identities and location information of MSNs. Thus it becomes necessary to address the aspect of data privacy while aggregation and analysis of such intermittent data from MSNs. The paper presents the previous work in the domain of privacy preserved architecture for such IoMT applications.