Edge Centric Secure Data Sharing with Digital Twins in Smart Ecosystems

TL;DR

This paper introduces a novel edge-centric security framework for IoT data sharing using digital twins with dynamic tagging to ensure real-time, secure, and efficient data access control in smart ecosystems.

Contribution

It proposes a new architecture utilizing multiple digital twins with dynamic tags for secure, real-time data separation and access control at the edge in IoT environments.

Findings

Effective data separation with dynamic tags enhances security.

Minimal overhead and low latency achieved in edge deployment.

Performance metrics confirm the lightweight nature of the solution.

Abstract

Internet of Things (IoT) is a rapidly growing industry currently being integrated into both consumer and industrial environments on a wide scale. While the technology is available and deployment has a low barrier of entry in future applications, proper security frameworks are still at infancy stage and are being developed to fit varied implementations and device architectures. Further, the need for edge centric mechanisms are critical to offer security in real time smart connected applications with minimal or negligible overhead. In this paper, we propose a novel approach of data security by using multiple device shadows (aka digital twins) for a single physical object. These twins are paramount to separate data among different virtual objects based on tags assigned on-the-fly, and are used to limit access to different data points by authorized users/applications only. The novelty of the proposed architecture resides in the attachment of dynamic tags to key-value pairs reported by physical devices in the system. We further examine the advantages of tagging data in a digital twin system, and the performance impacts of the proposed data separation scheme. The proposed solution is deployed at the edge, supporting low latency and real time security mechanisms with minimal overhead, and is light-weight as reflected by captured performance metrics.