Building Trust in Data for IoT Systems (2403.02225v2)

Abstract: Nowadays, Internet of Things platforms are being deployed in a wide range of application domains. Some of these include use cases with security requirements, where the data generated by an IoT node is the basis for making safety-critical or liability-critical decisions at system level. The challenge is to develop a solution for data exchange while proving and verifying the authenticity of the data from end-to-end. In line with this objective, this paper proposes a novel solution with the proper protocols to provide Trust in Data, making use of two Roots of Trust that are the IOTA Distributed Ledger Technology and the Trusted Platform Module. The paper presents the design of the proposed solution and discusses the key design aspects and relevant trade-offs. The paper concludes with a Proof-of-Concept implementation and an experimental evaluation to confirm its feasibility and to assess the achievable performance.

• The paper introduces an end-to-end trust framework that integrates IOTA Tangle with TPM-based remote attestation to secure IoT data.
• The paper details a proof-of-concept demonstrating TDT's feasibility with performance metrics on data writing and reading times.
• The paper outlines future research directions for scaling security in IoT systems and integrating with AI-driven analytics for enhanced reliability.

Overview of "Building Trust in Data for IoT Systems"

This paper addresses the pressing issue of data trustworthiness in Internet of Things (IoT) systems, which is particularly critical in contexts where data authenticity affects safety and liability-critical decision-making. Traditional approaches to secure communications and data integrity, such as Distributed Ledger Technology (DLT) and Transport Layer Security (TLS), are not sufficient to cover the end-to-end trust requirements, especially considering the integrity of software (SW) at the data source. The authors propose a comprehensive solution named Trusted Data over the Tangle (TDT) that leverages both IOTA's Tangle—a specific type of DLT—and trusted computing principles through hardware mechanisms like the Trusted Platform Module (TPM).

Key Contributions

1. End-to-End Trust Framework: The paper introduces a novel integration of DLT and trusted computing principles to achieve a more comprehensive trust model for IoT data. This involves remote attestation protocols to verify the integrity of IoT node software and DLTs that ensure data immutability.
2. Implementation and Evaluation: A Proof-of-Concept (PoC) is detailed to demonstrate the feasibility of the proposed system. The TDT combines IOTA Tangle's data anchoring capabilities with TPM-backed software integrity checks, thus ensuring not only data integrity from the moment of data generation to consumption but also the authenticity of data-producing nodes.
3. Performance Metrics: The authors conduct experiments to evaluate the performance of both the Remote Attestation (RA) and the Data Exchange protocols over the Tangle. Key performance indicators, such as data writing and reading times, highlight the limitations and potential optimizations of the proposed system. The results illustrate a realistic guidepost for practical implementation in scenarios where low latency and high throughput are critical.

Technical Implications

The proposed method offers robust measures against compromising attacks targeting IoT data authenticity and integrity. By utilizing TPM for software integrity and a highly secure network layer via the IOTA Tangle, the solution sidesteps common vulnerabilities faced by IoT systems.

The paper also opens up pathways for deeper integration between blockchain technologies and hardware-based security modules in IoT contexts. This fusion is pivotal for sectors that prioritize real-time data processing from heterogeneous IoT devices, such as autonomous transportation and critical infrastructure monitoring.

Future Trajectories in AI and IoT

While the paper's proof of concept establishes technical viability, future work should focus on scaling the TDT solution for larger IoT ecosystems involving diverse node capabilities. Additionally, investigating the trade-offs between performance and security in varying network conditions and devices constraints would be beneficial. Exploring how such systems might integrate with AI applications that demand high trust and reliability in data could significantly impact domains like AI-driven analytics and decision-making processes.

In summation, the research represents a meaningful step in ensuring data authenticity and integrity within the IoT field, using state-of-the-art cryptographic and distributed ledger technologies to provide a holistic security solution.