Novel Immutable Data Provenance in Embedded Systems: Integrating Critical Infrastructure Auditability and Monitoring via Anonymous Blockchain Technology

Abstract

Embedded systems are at the heart of critical infrastructure facilities in areas such as energy, transportation, healthcare, defense; where integrity, traceability, and auditability of data generated by the system are of great importance. However, existing data provenance security solutions for embedded settings are highly unsatisfactory because centralized design is vulnerable in embedded environment, they do not scale well and have poor privacy mechanisms. This work presents a novel method of combining immutable data provenance and anonymous blockchain solutions to solve these issues and promote the trustworthiness of embedded systems. The framework takes advantage of privacy-preserving cryptographic methods such as ring signatures, stealth addresses, and zero-knowledge proofs to support tamper-evident decentralized storage of data events without losing source privacy. It's designed to run efficiently under the resource constraints of the embedded platforms, to be low point compatible with low-power devices, without sacrificing the responsiveness of the system or the authenticity of the data. The architecture is designed for real time monitoring and auditability on distributed embedded devices that are installed in critical infrastructure networks. A lightweight consensus algorithm designed for embedded environments allows secure synchronization and validation of data without the need for the heavy computation of a public blockchain. The framework was experimentally validated through prototype implementation and simulation in multiple use-case scenarios, showing its effectiveness against data forgery, unauthorized access and provenance tampering. Performance evaluation demonstrates that the model is scalable, low latency and high throughput under restrained resource environments. This work demonstrated that, by building immutable and anonymous data provenance into embedded systems, in addition to increasing transparency, trustworthiness, and robustness of operation, it is also possible to lay the foundation for a novel class of secure, decentralized infrastructure monitoring tools suitable for adversarial deployments. Results demonstrate a robustness for deployment into actual applications with high-assured data traceability supported with privacy protection.