Advanced digital signatures for preserving privacy and trust management in hierarchical heterogeneous IoT: Taxonomy, capabilities, and objectives

Internet of Things (IoT) systems in different areas, such as manufacturing, transportation, and healthcare, are the convergence of several technologies. There are many concerns about security and privacy drawbacks in IoT systems. Apart from confidentiality supported by encryption primitives, authenticity and non-repudiation are of utmost importance. IoT entities generally use conventional digital signature schemes to achieve imperative goals. However, there are some state-of-the-art digital signatures with more functionalities, IoT-friendly properties, and privacy-preserving features.

This survey paper aims to accelerate the adoption of advanced digital signatures. We bridge the gap between the advanced theoretical digital signatures recently designed in cryptographic-oriented papers and the applied IoT systems. It aids researchers in achieving more security, privacy as well as some unique functionality aspects. First, we illustrate the benefits of the hierarchical and heterogeneous IoT architecture supporting the end-edge-fog-cloud continuum accompanying blockchain technology. Second, our survey delves into five state-of-the-art digital signatures, including randomizable, keyless, double-authentication-prevention, sanitizable, and redactable schemes, that are aligned with entities in IoT systems. We provide an outline, taxonomy, comparison table, and diverse IoT-based use cases for each of them. Then, the integration of primitives and the relationship diagrams give guidelines to help select the appropriate advanced digital signatures and highlights how researchers can use them with different IoT entities for preserving privacy and management of trust.

Digital signature, Internet of Things, Privacy-preserving, Blockchain, Cloud computing

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This research was supported, in part, by the Natural Sciences and Engineering Research Council (external link, opens in new window)  (NSERC). [Award number: RGPIN-2019-06150], Discovery Grant.