Blockchain-Based Decentralized Identification in IoT: An Overview of Existing Frameworks and Their Limitations
Abstract
:1. Introduction
- A list of features and specifications for decentralized identification is provided with an emphasis on Self-Sovereign Identity (SSI).
- A review of advanced research in decentralized identification frameworks is presented with an emphasis on the heterogeneity of online connected devices in IoT.
- Discuss and investigate the challenges associated with the use of decentralized identification approaches in the context of digital identity.
2. Methodology
2.1. Research Questions
- What are the key technical features of a blockchain-based decentralized identification system and how do they differ from traditional IdM systems?
- What are the potential benefits of implementing blockchain-based decentralized identification in the context of IoT?
- What are the practical challenges and obstacles that must be addressed to successfully deploy a blockchain-based decentralized identification system in IoT applications?
2.2. Search Strategy
2.3. Inclusion/Exclusion Criteria
2.3.1. Inclusion Criteria
- Articles introducing a new blockchain-based decentralized identification method for IoT use cases.
- Since recent articles are more likely to reflect current trends in research and advances in the field, we chose to include recent articles. Therefore, research published between 2018 and 2022 was considered.
- The scope of the research was limited to conference papers and journal articles.
2.3.2. Exclusion Criteria
- Survey, review, and press articles that did not provide specific technological contributions were excluded from the selection.
- We excluded studies that were outdated or did not contribute significantly to the current state of the field.
- Articles that were not in English.
2.4. Final Selection
3. Basic Concepts
- Public Blockchains: A public blockchain is a permissionless blockchain in which all participants can access the content of the blockchain and publish blocks. Bitcoin (https://bitcoin.org/en/ (accessed 13 February 2023)) and Ethereum (https://ethereum.org/en/ (accessed 13 February 2023)) are examples of public blockchains. To mitigate the risks associated with the use of public blockchains, a large number of anonymous nodes are needed to accommodate them.
- Private Blockchains: A private blockchain is a permissioned network. The entities are only allowed to join the network once their identities and other information are verified. Hyperledger Fabric (https://www.hyperledger.org/use/fabric (accessed 13 February 2023)) and R3 Corda (https://corda.net/ (accessed 13 February 2023)) are popular examples of private blockchains.
- Consortium Blockchains: These blockchains, also known as federated blockchains, are similar to private blockchains in that they are permissioned networks. Several organizations are participating in consortium networks that help maintain transparency between them.
- Hybrid Blockchain: In the context of blockchain technology, the term hybrid blockchain refers to a network that is a combination of the ideal parts of private and public blockchain solutions. In a hybrid blockchain, transactions and records are made private, but can be verified when involved, for example, by enabling access through contracts.
3.1. Decentralized Identifiers (DIDs)
3.2. Decentralized Authentication
3.3. Decentralized Authorization
3.4. Self-Sovereign Identity (SSI)
4. Decentralized Identity Solutions
4.1. Relaxed Operational Requirements
4.2. Constrained Operational Requirements
5. Discussion
- Blockchain Technology: The use of blockchain technology has become increasingly widespread in recent years. Due to the explosive popularity of blockchain technology, various blockchain platforms have emerged, each with its own advantages and disadvantages. Since the methods presented are based on Ethereum or Hyperledger Indy, they will be discussed from a variety of perspectives in the following.
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- Throughput: Each blockchain has the capacity to process a number of transactions per unit of time (second), named Throughput or Transactions Per Second (TPS). This character becomes increasingly critical where the ledger is required to support an increasing number of transactions per second. Therefore, decentralized identification approaches cannot be implemented in large-scale IoT networks, such as smart cities, if transactions take a considerable amount of time. According to reports, Hyperledger’s throughput is approximately 3500 TPS [46], while Ethereum is able to deliver around 14 TPS [47].
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- Latency: It is the time that has elapsed between the submission of a transaction and its confirmation by a blockchain network. When a device needs to transmit a large number of transactions in a short period of time, long delays can be problematic. This causes the device to run slower, affecting its performance. The latency of Ethereum has been shown to be higher than that of Hyperledger [48].
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- Cost: Running a smart contract on Ethereum is costly. Approaches related to this blockchain technology require a fee to execute smart contracts. A cost analysis of Ethereum is presented in [49], which concludes that “data storage in Ethereum is expensive”. Regarding Hyperledger Indy, it does not have the capability to support smart contracts.
- Scalability Evaluation: Scalability is one of the major problems faced by blockchain technologies and refers to the size of a blockchain network and the number of entities it can support. In IoT applications, a large number of entities need to communicate through the network to identify, authorize, authenticate, share data, or possibly execute smart contracts. Evaluations have indicated that common blockchain technologies are generally not suitable solutions for managing identity and access in large-scale networks that include limited devices due to their consensus mechanisms, which need to be modified [50]. Hence, decentralized identification approaches must present an efficient solution to handle the massive amounts of data collected by a large network of entities. A number of approaches can be employed to achieve scalability in blockchain networks, including sharding, horizontal, and vertical scaling [51]. During sharding, the data are partitioned and distributed among the nodes in order to allow parallel processing. On horizontal scaling, more nodes are added to the network in order to increase network capacity. Vertical scaling refers to adding more processing power and memory to a single node so that its performance can be enhanced. The achievement of scalability in a blockchain network requires maintaining a delicate balance between keeping sufficient computational resources available and minimizing costs associated with transaction processing. However, only Kassem et al. [12] evaluated the scalability of their solution. They examined the scalability of their system by the amount of energy consumption, and their results showed a linear relationship between cost and system operations, where the cost remains constant as the frequency of operations increases.Regarding the scalability evaluation of the presented blockchain networks, Ethereum has faced scalability issues due to its consensus algorithm (low TPS, high latency, and high cost). In contrast, Hyperledger uses a modular architecture, which permits different consensus algorithms and components to be integrated according to specific use cases. Furthermore, compared to public blockchain networks such as Ethereum, Hyperledger provides support for private and permissioned blockchain networks, which offer greater control over the network and superior performance [52].
- Security Evaluation: It is stated that the decentralization of the identification process can increase the level of privacy and security, allowing selective disclosure of information and protection against data manipulation [53]. Securing a network with billions of devices, where many of them have limited resources and cannot perform heavy calculations is a challenge considering that current state-of-the-art security protocols are based on a high degree of complexity [54,55]. In this regard, a practical decentralized approach needs immunity from all viable attacks and, at the same time, does not depend on heavy computing. Despite the importance of security aspects, such as privacy and anonymity, only Kassem et al. [12] and Zhang et al. [11] considered this issue in their solution and claimed to be able to avoid conventional security threats. These approaches enable authentication protection to resist different attacks and provide ownership protection by using some digital signature algorithm and keeping identities and their information in a secure and authorized ledger.Regarding the security evaluations of Ethereum and Hyperledger, these blockchain platforms employ different security methodologies. Overall, both platforms are robustly secured and have been thoroughly evaluated. As Hyperledger uses permissioned blockchain frameworks and focuses on enterprise security, it prioritizes enterprise security, while Ethereum provides excellent security guarantees, but it is vulnerable to attacks as it is a public blockchain platform [56].
- Trust Evaluation: It has always been a main topic of interest in the research community on how to promote and manage trust in distributed systems. The term trust refers to the ability of an entity to engage in an interactive relationship based on certain expectations and taking risks [57]. There is the possibility that interactions and collaborations may be unreliable due to the use of malicious behavior by some entities. Blockchain technology presents a potential alternative to transferring trust between entities to a protocol [58]. However, there are many malicious activities, such as phishing attacks, smart contract attacks, and consensus mechanism attacks, that target trust on blockchain technologies [59,60]. Furthermore, given the diversity and scale of the IoT system, trustworthiness is an important concern that must be addressed both in the design stage and during the implementation phase [61]. Therefore, to achieve a decentralized trusted identification framework, IoT must be seamlessly integrated with blockchain technology. Trust evaluation was not explicitly referenced in any of the articles reviewed.Regarding the trust evaluations of Ethereum and Hyperledger, although both platforms are designed to address different use cases and have different technical architectures, trust is a common element for both. The trustworthiness of Ethereum is spread among a large network of validators or miners who collaborate to maintain the credibility of the platform. However, the trustworthiness of Hyperledger is based on the credibility of its network participants who must verify their identity before being granted access to the network. Thus, the level of trust of Hyperledger is more centralized than that of Ethereum, as it relies on a smaller group of reliable participants to ensure the integrity of the blockchain [62].
- Offline IoTs: It should be noted that blockchain-based DID-IdM methods require online ledger operations, making them incompatible with offline IoT environments. DIDs are designed to provide proof of identity and full control over the identity in a secure and user-friendly manner between entities, regardless of whether the network is online or offline. The idea of placing SSI-based DID offline has recently been researched, but the methods are still in their infancy. For example, Alexander et al. [63] developed an offline mobile access control system based on SSI, DIDs, and VCs that uses Bluetooth Low Energy (BLE) to transfer data without depending on a certain transport-specific protocol. However, these offline methods raise concerns that must be addressed, including ensuring that the device is connected to the correct DID device when initializing the connection and designing a feasible data flow between entities for practical applications. Finally, none of the reviewed approaches provide a solution to the problem of serving a large number of devices in offline mode. In other words, all approaches assume that the device is connected to the Internet, even if that connection is limited.
- Technical: Currently, the most significant technical challenge facing decentralized identification approaches is their infancy and inability to be widely adopted. Despite the continued efforts to add new features and resolve existing issues, none of the identified frameworks has a substantial adoption base, making it difficult to predict the impact on the scalability of billions of heterogeneous devices spread across multiple environments. In addition, there is no specific standard for writing smart contracts, raising some concerns about data security. Smart contracts are immutable and heavily depend on developers, who can make mistakes and expose smart contracts to bugs. This makes any mistake costly for entities and allows saboteurs to steal and misuse digital assets.
- Best Practices and Standardization: Currently, a comprehensive and sustainable decentralized identification approach is not available for use in borderless environments for different types of entity. This implies that, without the reliance on a central authority, the entity always has access to the identification method, regardless of geography, the laws, and the type of connection. In general, efforts should be directed at consolidating the existing specifications and establishing best practices that will not only meet these specifications, but also reflect on the supporting frameworks to ensure that the GDPR requirements are met in full.
- Central Authoritarians: As stated above, decentralized identification eliminates the power of central authoritarians. However, governments and large corporations will be the main obstacle to these changes. Large companies and governments can try to enforce strict regulations on decentralized mechanisms, such as decentralized identification, which means that they can demand access to protected data. Furthermore, since they have financial resources, they may decide to introduce their own technology, which competes with other decentralized mechanisms that still give them an element of control. It can be concluded that decentralized identification, although well designed and implemented, will not be very effective without the support of governments and large corporations.
6. Conclusions and Future Work
- The interoperability of various systems and platforms is essential. Currently, there is no interoperability between the existing solutions. Efforts should be made in the future work to achieve interoperability among various systems, so that entities can utilize their digital identities across a wide range of platforms.
- Although decentralized systems can generally be considered more secure than traditional systems, the development of new cryptographic techniques or the integration of biometric authentication techniques into decentralized systems is left to future work.
- The adoption of the new approach will be one of the biggest challenges to its success. The effectiveness of decentralized identification depends on its wide adoption and integration into a wide range of services and platforms. Efforts can be directed toward promoting the benefits of decentralized identification and making adoption easier for government, individuals, and organizations in the future.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
ACE | Authentication and Authorization for Constrained |
AS | Authorization Server |
BLE | Bluetooth Low Energy |
CA | Central Authorities |
CBOR | Concise Binary Object Representation |
CoAP | Constrained Application Protocol |
COSE | Object Signing and Encryption |
DID | Decentralized Identifier |
FIM | Federated Identity Management |
IdM | Identity Management |
IdP | Identity Provider |
IIoT | Industrial Internet of Things |
IoT | Internet of Things |
OAuth | Open Authorization |
RS | Resource Servers |
SC | Smart Contracts |
SSI | Self-Sovereign Identity |
UDP | User Datagram Protocol |
VC | Verifiable Credential |
References
- Cisco. Cisco Annual Internet Report (2018–2023) White Paper; Cisco: San Jose, CA, USA, 2020. [Google Scholar]
- Anitha, A.; Haritha, T. The Integration of Blockchain With IoT in Smart Appliances: A Systematic Review. Blockchain Technologies for Sustainable Development in Smart Cities. IGI Global, 2022; pp. 223–246. Available online: https://www.igi-global.com/chapter/the-integration-of-blockchain-with-iot-in-smart-appliances/297436 (accessed on 13 February 2023).
- Grassi, P.; Garcia, M.; Fenton, J. Digital identity guidelines. NIST Spec. Publ. 2017, 800, 63-3. [Google Scholar]
- Weerapanpisit, P.; Trilles, S.; Huerta, J.; Painho, M. A Decentralized Location-Based Reputation Management System in the IoT Using Blockchain. IEEE Internet Things J. 2022, 9, 15100–15115. [Google Scholar] [CrossRef]
- Michailidis, E.T.; Vouyioukas, D. A Review on Software-Based and Hardware-Based Authentication Mechanisms for the Internet of Drones. Drones 2022, 6, 41. [Google Scholar] [CrossRef]
- Xu, M.; Chen, X.; Kou, G. A systematic review of blockchain. Financ. Innov. 2019, 5, 27. [Google Scholar] [CrossRef] [Green Version]
- Gilani, K.; Bertin, E.; Hatin, J.; Crespi, N. A survey on blockchain-based identity management and decentralized privacy for personal data. In Proceedings of the 2020 2nd Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS), Paris, France, 28–30 September 2020; pp. 97–101. [Google Scholar]
- Alanzi, H.; Alkhatib, M. Towards Improving Privacy and Security of Identity Management Systems Using Blockchain Technology: A Systematic Review. Appl. Sci. 2022, 12, 12415. [Google Scholar] [CrossRef]
- Alharbi, M.; Hussain, F.K. Blockchain-based identity management for personal data: A survey. In Advances on Broad-Band Wireless Computing, Communication and Applications. BWCCA 2021; Springer: Cham, Switzerland, 2022; pp. 167–178. [Google Scholar]
- Liu, Y.; He, D.; Obaidat, M.S.; Kumar, N.; Khan, M.K.; Choo, K.K.R. Blockchain-based identity management systems: A review. J. Netw. Comput. Appl. 2020, 166, 102731. [Google Scholar] [CrossRef]
- Zhang, K.; Yu, J.; Lin, C.; Ning, J. Blockchain-based access control for dynamic device management in microgrid. Peer-Netw. Appl. 2022, 15, 1653–1668. [Google Scholar] [CrossRef]
- Alsayed Kassem, J.; Sayeed, S.; Marco-Gisbert, H.; Pervez, Z.; Dahal, K. DNS-IdM: A Blockchain Identity Management System to Secure Personal Data Sharing in a Network. Appl. Sci. 2019, 9, 2953. [Google Scholar] [CrossRef] [Green Version]
- Lagutin, D.; Kortesniemi, Y.; Fotiou, N.; Siris, V.A. Enabling Decentralised Identifiers and Verifiable Credentials for Constrained Internet-of-Things Devices using OAuth-based Delegation. In Proceedings of the Workshop on Decentralized IoT Systems and Security (DISS 2019), in Conjunction with the NDSS Symposium, San Diego, CA, USA, 24 February 2019. [Google Scholar]
- Alphand, O.; Amoretti, M.; Claeys, T.; Dall’Asta, S.; Duda, A.; Ferrari, G.; Rousseau, F.; Tourancheau, B.; Veltri, L.; Zanichelli, F. IoTChain: A blockchain security architecture for the Internet of Things. In Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, Spain, 15–18 April 2018; pp. 1–6. [Google Scholar]
- Siris, V.A.; Dimopoulos, D.; Fotiou, N.; Voulgaris, S.; Polyzos, G.C. Interledger smart contracts for decentralized authorization to constrained things. In Proceedings of the IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Paris, Francem, 29 April–2 May 2019; pp. 336–341. [Google Scholar]
- Agi, M.A.; Jha, A.K. Blockchain technology in the supply chain: An integrated theoretical perspective of organizational adoption. Int. J. Prod. Econ. 2022, 247, 108458. [Google Scholar] [CrossRef]
- Ali, M.S.; Vecchio, M.; Pincheira, M.; Dolui, K.; Antonelli, F.; Rehmani, M.H. Applications of blockchains in the Internet of Things: A comprehensive survey. IEEE Commun. Surv. Tutor. 2018, 21, 1676–1717. [Google Scholar] [CrossRef]
- Alkhateeb, A.; Catal, C.; Kar, G.; Mishra, A. Hybrid blockchain platforms for the internet of things (IoT): A systematic literature review. Sensors 2022, 22, 1304. [Google Scholar] [CrossRef]
- Capocasale, V.; Perboli, G. Standardizing smart contracts. IEEE Access 2022, 10, 91203–91212. [Google Scholar] [CrossRef]
- Sporny, M.; Longley, D.; Sabadello, M.; Reed, D.; Steele, O.; Allen, C. Decentralized Identifiers (DIDs) v1.0 Core architecture, data model, and representations. W3C Working Draft. 2022. Available online: https://www.w3.org/TR/did-core/ (accessed on 13 February 2023).
- Sporny, M.; Noble, G.; Longley, D.; Burnett, D.C.; Zundel, B.; Hartog, K.D. Verifiable Credentials Data Model v1. 1. World Wide Web Consortium. 2022. Available online: https://www.w3.org/TR/vc-data-model/ (accessed on 13 February 2023).
- Shi, J.; Zeng, X.; Han, R. A Blockchain-Based Decentralized Public Key Infrastructure for Information-Centric Networks. Information 2022, 13, 264. [Google Scholar] [CrossRef]
- Zhaofeng, M.; Jialin, M.; Jihui, W.; Zhiguang, S. Blockchain-based decentralized authentication modeling scheme in edge and IoT environment. IEEE Internet Things J. 2020, 8, 2116–2123. [Google Scholar] [CrossRef]
- Trnka, M.; Abdelfattah, A.S.; Shrestha, A.; Coffey, M.; Cerny, T. Systematic Review of Authentication and Authorization Advancements for the Internet of Things. Sensors 2022, 22, 1361. [Google Scholar] [CrossRef]
- Chen, F.; Xiao, Z.; Cui, L.; Lin, Q.; Li, J.; Yu, S. Blockchain for Internet of Things applications: A review and open issues. J. Netw. Comput. Appl. 2020, 172, 102839. [Google Scholar] [CrossRef]
- Bai, Y.; Lei, H.; Li, S.; Gao, H.; Li, J.; Li, L. Decentralized and Self-Sovereign Identity in the Era of Blockchain: A Survey. In Proceedings of the 2022 IEEE International Conference on Blockchain (Blockchain), Espoo, Finland, 22–25 August 2022; pp. 500–507. [Google Scholar]
- Bartolomeu, P.C.; Vieira, E.; Hosseini, S.M.; Ferreira, J. Self-Sovereign Identity: Use-cases, Technologies, and Challenges for Industrial IoT. In Proceedings of the 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Zaragoza, Spain, 10–13 September 2019; pp. 1173–1180. [Google Scholar]
- Mühle, A.; Grüner, A.; Gayvoronskaya, T.; Meinel, C. A survey on essential components of a self-sovereign identity. Comput. Sci. Rev. 2018, 30, 80–86. [Google Scholar] [CrossRef] [Green Version]
- Grande, E.; Beltrán, M. Edge-centric delegation of authorization for constrained devices in the Internet of Things. Comput. Commun. 2020, 160, 464–474. [Google Scholar] [CrossRef]
- Restuccia, G.; Tschofenig, H.; Baccelli, E. Low-power IoT communication security: On the performance of DTLS and TLS 1.3. In Proceedings of the 9th IFIP International Conference on Performance Evaluation and Modeling in Wireless Networks (PEMWN), Berlin, Germany, 1–3 December 2020; pp. 1–6. [Google Scholar]
- Ameer, S.; Benson, J.; Sandhu, R. An Attribute-Based Approach toward a Secured Smart-Home IoT Access Control and a Comparison with a Role-Based Approach. Information 2022, 13, 60. [Google Scholar] [CrossRef]
- Dehalwar, V.; Kolhe, M.L.; Deoli, S.; Jhariya, M.K. Blockchain-based trust management and authentication of devices in smart grid. Clean. Eng. Technol. 2022, 8, 100481. [Google Scholar] [CrossRef]
- Venkatraman, S.; Parvin, S. Developing an IoT Identity Management System Using Blockchain. Systems 2022, 10, 39. [Google Scholar] [CrossRef]
- Geetha, R.; Padmavathy, T.; Umarani Srikanth, G. A Scalable Block Chain Framework for User Identity Management in a Decentralized Network. Wirel. Pers. Commun. 2022, 123, 3719–3736. [Google Scholar] [CrossRef]
- Seitz, L.; Selander, G.; Wahlstroem, E.; Erdtman, S.; Tschofenig, H. Authentication and Authorization for Constrained Environments (ACE) using the OAuth 2.0 Framework (ACE-OAuth). Internet Engineering Task Force (IETF). August 2022. Available online: https://www.rfc-editor.org/rfc/rfc9200.pdf (accessed on 13 February 2023).
- Amsüss, C.; Mattsson, J.P.; Selander, G. Constrained Application Protocol (CoAP): Echo, Request-Tag, and Token Processing. Internet Engineering Task Force (IETF). February 2022. Available online: https://www.rfc-editor.org/rfc/rfc9175.pdf (accessed on 13 February 2023).
- Bormann, C.; Hoffman, P. Concise Binary Object Representation (CBOR). Internet Engineering Task Force (IETF). December 2020. Available online: https://www.rfc-editor.org/rfc/rfc8949.pdf (accessed on 13 February 2023).
- Patel, S.; Sahoo, A.; Mohanta, B.K.; Panda, S.S.; Jena, D. DAuth: A decentralized web authentication system using Ethereum based blockchain. In Proceedings of the International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), Vellore, India, 30–31 March 2019; pp. 1–5. [Google Scholar]
- Maler, E.; Machulak, M.; Richer, J. User-Managed Access (UMA) 2.0 Grant for OAuth 2.0 Authorization. Kantara Initiative, Draft Recommendation. 2017. Available online: https://docs.kantarainitiative.org/uma/wg/rec-oauth-uma-grant-2.0.html (accessed on 13 February 2023).
- Hardjono, T. Decentralized Service Architecture for OAuth2.0. IETF Draft. 2018. Available online: https://datatracker.ietf.org/doc/pdf/draft-hardjono-oauth-decentralized-02 (accessed on 13 February 2023).
- Biswas, S. Enhancing the Privacy of Decentralized Identifiers with Ring Signatures. 2020. Available online: https://aaltodoc.aalto.fi/bitstream/handle/123456789/46100/master_Biswas_Shamim_2020.pdf?sequence=1&isAllowed=y (accessed on 13 February 2023).
- Kortesniemi, Y.; Lagutin, D.; Elo, T.; Fotiou, N. Improving the privacy of iot with decentralised identifiers (dids). J. Comput. Netw. Commun. 2019, 2019, 8706760. [Google Scholar] [CrossRef] [Green Version]
- Claeys, T.; Rousseau, F.; Tourancheau, B. Securing complex IoT platforms with token based access control and authenticated key establishment. In Proceedings of the International Workshop on Secure Internet of Things (SIoT), Oslo, Norway, 15 September 2017; pp. 1–9. [Google Scholar]
- Vučinić, M.; Tourancheau, B.; Rousseau, F.; Duda, A.; Damon, L.; Guizzetti, R. OSCAR: Object security architecture for the Internet of Things. Ad Hoc Netw. 2015, 32, 3–16. [Google Scholar] [CrossRef] [Green Version]
- Rams, T.; Pacyna, P. A survey of group key distribution schemes with self-healing property. IEEE Commun. Surv. Tutor. 2012, 15, 820–842. [Google Scholar] [CrossRef]
- Gorenflo, C.; Lee, S.; Golab, L.; Keshav, S. FastFabric: Scaling hyperledger fabric to 20,000 transactions per second. Int. J. Netw. Manag. 2020, 30, e2099. [Google Scholar] [CrossRef] [Green Version]
- Kılıç, B.; Özturan, C.; Sen, A. Parallel analysis of Ethereum blockchain transaction data using cluster computing. Clust. Comput. 2022, 25, 1885–1898. [Google Scholar] [CrossRef]
- Dinh, T.T.A.; Wang, J.; Chen, G.; Liu, R.; Ooi, B.C.; Tan, K.L. Blockbench: A framework for analyzing private blockchains. In Proceedings of the 2017 ACM International Conference on Management of Data, Chicago, IL, USA, 14–19 May 2017; pp. 1085–1100. [Google Scholar]
- Kostamis, P.; Sendros, A.; Efraimidis, P. Exploring Ethereum’s Data Stores: A Cost and Performance Comparison. In Proceedings of the Blockchain Research & Applications for Innovative Networks and Services (BRAINS), Paris, France, 27–30 September 2021; pp. 53–60. [Google Scholar]
- Puthal, D.; Mohanty, S.P.; Yanambaka, V.P.; Kougianos, E. Poah: A novel consensus algorithm for fast scalable private blockchain for large-scale iot frameworks. arXiv 2020, arXiv:2001.07297. [Google Scholar]
- Diaconita, V.; Belciu, A.; Stoica, M.G. Trustful Blockchain-Based Framework for Privacy Enabling Voting in a University. J. Theor. Appl. Electron. Commer. Res. 2023, 18, 8. [Google Scholar] [CrossRef]
- Malik, H.; Manzoor, A.; Ylianttila, M.; Liyanage, M. Performance analysis of blockchain based smart grids with Ethereum and Hyperledger implementations. In Proceedings of the 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), Goa, India, 16–19 December 2019; pp. 1–5. [Google Scholar]
- Elisa, N.; Yang, L.; Chao, F.; Cao, Y. A framework of blockchain-based secure and privacy-preserving E-government system. Wireless Netw. 2018, 1–11. [Google Scholar] [CrossRef] [Green Version]
- Salimitari, M.; Chatterjee, M. A survey on consensus protocols in blockchain for iot networks. arXiv 2018, arXiv:1809.05613. [Google Scholar]
- Bouraga, S. A taxonomy of blockchain consensus protocols: A survey and classification framework. Expert Syst. Appl. 2021, 168, 114384. [Google Scholar] [CrossRef]
- Abhishek, P.; Narayan, D.; Altaf, H.; Somashekar, P. Performance Evaluation of Ethereum and Hyperledger Fabric Blockchain Platforms. In Proceedings of the 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India, 3–5 October 2022; pp. 1–5. [Google Scholar]
- Mayer, R.C.; Davis, J.H.; Schoorman, F.D. An integrative model of organizational trust. Acad. Manag. Rev. 1995, 20, 709–734. [Google Scholar] [CrossRef]
- Ranathunga, T.; Marfievici, R.; McGibney, A.; Rea, S. A DLT-based trust framework for IoT ecosystems. In Proceedings of the 2020 International Conference on Cyber Security and Protection of Digital Services (Cyber Security), Dublin, Ireland, 15–19 June 2020; pp. 1–8. [Google Scholar]
- Moubarak, J.; Filiol, E.; Chamoun, M. On blockchain security and relevant attacks. In Proceedings of the IEEE Middle East and North Africa Communications Conference (MENACOMM), Jounieh, Lebanon, 18–20 April 2018; pp. 1–6. [Google Scholar]
- Haugum, T.; Hoff, B.; Alsadi, M.; Li, J. Security and Privacy Challenges in Blockchain Interoperability-A Multivocal Literature Review. In Proceedings of the International Conference on Evaluation and Assessment in Software Engineering, Gothenburg, Sweden, 13–15 June 2022; pp. 347–356. [Google Scholar]
- Rodrigues, B.; Franco, M.; Killer, C.; Scheid, E.J.; Stiller, B. On Trust, Blockchain, and Reputation Systems. In Handbook on Blockchain; Springer: Cham, Switzerland, 2022; pp. 299–337. [Google Scholar]
- Zhong, B.; Wu, H.; Ding, L.; Luo, H.; Luo, Y.; Pan, X. Hyperledger fabric-based consortium blockchain for construction quality information management. Front. Eng. Manag. 2020, 7, 512–527. [Google Scholar] [CrossRef]
- Enge, A.; Satybaldy, A.; Nowostawski, M. An offline mobile access control system based on self-sovereign identity standards. Comput. Netw. 2022, 219, 109434. [Google Scholar] [CrossRef]
Zhang et al. [11] | Kassem et al. [12] | Lagutin et al. [13] | Alphand et al. [14] | Siris et al. [15] | |
---|---|---|---|---|---|
Blockchain Technology | Ethereum | Ethereum (permissionless and permissioned) | Hyperledger Indy | Ethereum (permissioned) | Ethereum (permissioned) |
Scalability Evaluation | ✗ | ✓ | ✗ | ✗ | ✗ |
Security Evaluation | Resists Against: - Collusion - Replay Attack - Modification Attack - Masquerade Attack | Protection of: - Authentication - Ownership | Not provided | Not provided | Not provided |
Trust Evaluation | ✗ | ✗ | ✗ | ✗ | ✗ |
Offline IoTs | ✗ | ✗ | ✗ | ✗ | ✗ |
Advantage | - Dynamic Access - Low time delay | - Limited Devices - Privacy protection - Real-world identity | - Limited Devices - Dynamic Access - Privacy protection | - Limited Devices - Privacy protection | - Limited Devices - Data Reduction |
Disadvantage | - Costly - Lack of support for limited devices | - Costly - Lack of support for limited devices | - Lack of scalability - No evaluation report | - Costly - High delay | - Costly - High delay |
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Hosseini, S.M.; Ferreira, J.; Bartolomeu, P.C. Blockchain-Based Decentralized Identification in IoT: An Overview of Existing Frameworks and Their Limitations. Electronics 2023, 12, 1283. https://doi.org/10.3390/electronics12061283
Hosseini SM, Ferreira J, Bartolomeu PC. Blockchain-Based Decentralized Identification in IoT: An Overview of Existing Frameworks and Their Limitations. Electronics. 2023; 12(6):1283. https://doi.org/10.3390/electronics12061283
Chicago/Turabian StyleHosseini, Seyed Mohammad, Joaquim Ferreira, and Paulo C. Bartolomeu. 2023. "Blockchain-Based Decentralized Identification in IoT: An Overview of Existing Frameworks and Their Limitations" Electronics 12, no. 6: 1283. https://doi.org/10.3390/electronics12061283
APA StyleHosseini, S. M., Ferreira, J., & Bartolomeu, P. C. (2023). Blockchain-Based Decentralized Identification in IoT: An Overview of Existing Frameworks and Their Limitations. Electronics, 12(6), 1283. https://doi.org/10.3390/electronics12061283