Design of improved attribute based encryption scheme for fog computing environment

Abstract

The exponential increase in the number of connected devices within the IoT structure poses unprecedented challenges, especially in the field of cybersecurity. These devices have become increasingly vulnerable to cyber-attacks, ranging from simple exploits to sophisticated actions by competent adversaries. However, current security measures often fall short in adequately addressing the unique problems presented by the dynamic and diverse features associated with the IoT environment. Conventional modeling methodologies can be resource-intensive, posing challenges when applied to cloud devices with limited processing capabilities. Many businesses, entertainment, and government sector services require geographical data to provide information and security to users. In cloud computing, users can use location services by sending requests to the cloud, potentially leading to location privacy leakage and other vulnerabilities whereas in fog computing these services are provided by fog nodes. Being heterogenous various devices are also added at every stage in various applications so it is important to provide security. A complete solution to security is yet to be developed in these systems to overcome constraints including IoT devices. This thesis aims to design and implement a secure algorithm that reduces high computation and communication overhead and preserve privacy in fog computing environment. The security will help to preserve the personal data of the user from being compromised, at the same time maintaining the efficiency in terms of computation and communication cost. The following thesis revolves around the use case scenarios of three applications where design of secure fog computing using ABE has been implemented. The performance indicators of some ABE schemes such as CPU Efficacy of Data Generator, Bandwidth Effectiveness of Authority Key and Bandwidth Effectiveness of Data Generator have been discussed. A comparative analysis of this kind helps clarify performance related issues and gives us more clarity on h