Security and Privacy Aware Programming Model for IoT Applications in Cloud Environment

Abstract

The introduction of Internet of Things (IoT) applications into daily life has raised serious privacy concerns among consumers, network service providers, device manufacturers, and other parties involved. This paper gives a high-level overview of the three phases of data collecting, transmission, and storage in IoT systems as well as current privacy-preserving technologies. The following elements were investigated during these three phases:(1) Physical and data connection layer security mechanisms(2) Network remedies(3) Techniques for distributing and storing data. Real-world systems frequently have multiple phases and incorporate a variety of methods to guarantee privacy. Therefore, for IoT research, design, development, and operation, having a thorough understanding of all phases and their technologies can be beneficial. In this Study introduced two independent methodologies namely generic differential privacy (GenDP) and Cluster-Based Differential privacy ( Cluster-based DP) algorithms for handling metadata as intents and intent scope to maintain privacy and security of IoT data in cloud environments. With its help, we can virtual and connect enormous numbers of devices, get a clearer understanding of the IoT architecture, and store data eternally. However, due of the dynamic nature of the environment, the diversity of devices, the ad hoc requirements of multiple stakeholders, and hardware or network failures, it is a very challenging task to create security-, privacy-, safety-, and quality-aware Internet of Things apps. It is becoming more and more important to improve data privacy and security through appropriate data acquisition. The proposed approach resulted in reduced loss performance as compared to Support Vector Machine (SVM) , Random Forest (RF) .