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13 February 2023

Peer-to-Peer User Identity Verification Time Optimization in IoT Blockchain Network

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Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
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Author to whom correspondence should be addressed.
This article belongs to the Special Issue Cybersecurity and Reliability for 5G and Beyond and IoT Applications

Abstract

Blockchain introduces challenges related to the reliability of user identity and identity management systems; this includes detecting unfalsified identities linked to IoT applications. This study focuses on optimizing user identity verification time by employing an efficient encryption algorithm for the user signature in a peer-to-peer decentralized IoT blockchain network. To achieve this, a user signature-based identity management framework is examined by using various encryption techniques and contrasting various hash functions built on top of the Modified Merkle Hash Tree (MMHT) data structure algorithm. The paper presents the execution of varying dataset sizes based on transactions between nodes to test the scalability of the proposed design for secure blockchain communication. The results show that the MMHT data structure algorithm using SHA3 and AES-128 encryption algorithm gives the lowest execution time, offering a minimum of 36% gain in time optimization compared to other algorithms. This work shows that using the AES-128 encryption algorithm with the MMHT algorithm and SHA3 hash function not only identifies malicious codes but also improves user integrity check performance in a blockchain network, while ensuring network scalability. Therefore, this study presents the performance evaluation of a blockchain network considering its distinct types, properties, components, and algorithms’ taxonomy.

1. Introduction

The Internet of Things (IoT) is a technology-related concept in which devices which are used daily, including appliances, watches, etc., are connected to the Internet. The interconnection of IoT services is considered the central enabling technology for smart cities [1], which will revolutionize the way we conduct and manage business, critical infrastructure, healthcare, education, and entertainment in a secure and protected manner. As an essential application of IoT, a smart building (SB) automation system aims to incorporate equipment with sensors, actuators, and control devices to achieve operational efficiency and reliability, while significantly reducing operating costs. IoT devices’ lack of computational resources makes them unsuitable for intensive operations or large storage. This motivates the use of blockchain for IoT device management.
A blockchain is a distributed database of verifiable records containing transactions shared among participating parties and verified through consensus, where cryptographic hashes link the records within. In a heterogeneous blockchain network, the network must be identified and the identity allocated to different IoT nodes and individual users [2]. Digital identity, which is used to develop all the protocols related to security mechanisms, is one of the core concepts within security. Meanwhile, identity and access management (IAM) systems are useful for managing identity information with the help of operations set, such as register, revoke, look-up, and update functions. The IAM system holds various challenges. However, one of the main challenges is that IAM within IoT recognizes unfalsified identities attached to IoT appliances as a source of truth for user authentication and abnormal behaviour detection [1,3]. A recent review of security issues, challenges, and recommendations for blockchain technology has been presented in [4].
When managing IoT identities, initially, there is a need to recognize IoT devices and then allocate them to different identities available across the domain of the IoT, enforce security policies, and control their attitude or behaviour; all with the help of authentication and access control mechanisms [5]. For this reason, an identity verification framework based on blockchain technology could be utilized, which is one of the user-centric approaches toward managing the identities of IoT and facilitating their monitoring. In particular, blockchain is used to maintain all owners’ identities. However, identities associated with things are interrelated with the owner’s digital signature through the owner’s private key. The blockchain-based framework involves a methodology to filter, characterize, and monitor the appliances to extract digital signatures from the digital characteristics of the device [6]. Digital signatures based on identities and timestamps give blockchain an option for protecting, proving, and complying with rules, and auditing non-repudiation in data-intensive applications and ecosystems [7].
Thus, to make the smart home blockchain network more secure, non-interactive zero-knowledge proofs are considered a major building block, depicting the statement’s validity without disclosing any significant information. The zero-knowledge proof (ZKP) is one of the cryptographic techniques that demonstrates how a prover can confirm any particular statement without giving the verifier any vital information or disclosing information related to the witness. Apart from blockchain, the zero-knowledge protocol is an essential and versatile algorithm used for several privacy-oriented applications such as ethical behaviour and authentication systems [8].
Therefore, this article has the following significant contributions:
  • Identity management (IdM) system design based on a blockchain with a specific criterion to ensure user integrity and system performance.
  • Comparison of verification time with different user signature encryption algorithms using realistic datasets.
  • Selection of the optimum identity claim between encryption and hashing algorithms by considering network scalability and performance.
The rest of this paper is organized as follows. In Section 2, we present a brief background on integrity management, monitoring, and logging to highlight the identified authentication and key management system, relevant encryption algorithms, and streaming techniques that help to achieve this work. Section 3 describes the proposed architecture used in the blockchain for the IoT network and the process flowchart. Section 4 presents the implementation of the proposed user identity validation algorithm to analyze the execution time and the scalability of the network’s performance. Finally, in Section 5, we conclude with a detailed discussion of different issues involved in the proposed algorithms and the proposition to improve them.

3. System Model

3.1. User Identity Architecture Components

In this work, we propose an improved registration process [34] for the blockchain network with identity provider, as shown in Figure 1.
Figure 1. Proposed device registration process.
The device registration process in the blockchain network starts from the identity provider that enables this device or node to have the credentials in the blockchain network before creating the smart contract. Next, the service provider invokes the device information from the identity provider to authorize the associated privacy policies. Users obtain device information and privacy policies from the public variables of the identity provider. Therefore, the identity provider provides the addresses to the blockchain validator, who can then submit a request to bind the device to the device smart contract using the identity provider’s addresses, ensuring that the identity provider accepts the request and receives alerts. In addition, a combination of logging tools and real-time monitoring systems can be used to maintain optimal blockchain performance based on feedback from different components.
It is worthwhile to mention some limitations we didn’t consider in this work, like data maintenance using decryption of the original data blocks of the transaction before the hashing process.
In a decentralized permissioned blockchain network, users, or identity of things (IDoT), could be humans or smart devices interacting with each other or the sensors. All information is stored in the distributed ledger in the smart contract and accessed only by authorized nodes. Privacy and integrity are provided by several cryptographic algorithms. We proposed an identity management system using a symmetric or asymmetric algorithm and a digital signature for encryption and authentication.
Figure 2 shows the system design and workflow of our proposed blockchain network for IAM. The identity provider is responsible for permitting the participants, such as Alice, Bob, and a validator in our scenario to the network. Moreover, it emits identity claims about the network users. The service provider manages the permission to use the network. Meanwhile, the smart contract is the central core of the blockchain network in which all participants (i.e., Alice, Bob, validators, and any other node in the network) can immediately ascertain the outcome of the IAM procedure, without any intermediary’s involvement or time loss. The signature is created using an encryption method utilizing the private key, and the signature with hashing is used to verify the user’s identity in the validation process. In addition to handling the identity verification process, smart contracts also guarantee network transactions. Using one of the verification algorithms, the validator’s role is to ensure user integrity in case of falsified identity claims.
Figure 2. Architecture of proposed permissioned blockchain network identity management system.

3.2. Data Structure and Hashing Using Merkle Hash Tree

The transaction values were hashed into the transactions chain until the final transaction value was obtained. In a blockchain, the Merkle hash tree (MHT) algorithm is used to hash the data block and any transaction action added to the structure, as illustrated in Figure 3. Each block connects to the next block and block data structure and is shown in Figure 4.
Figure 3. Transaction structure.
Figure 4. Blocks structure.
In this work, we compare the conventional MHT with our proposed modified Merkle hash tree (MMHT), as shown in Figure 5 and Figure 6. In general, the mathematical calculation of the MHT data structure is modified in MMHT to gain 30% of time optimization. This is achieved by separating the chain of transactions into concatenated hash transactions (CHT) and MHT and then combining them to obtain the final block of transactions [35], which is represented mathematically for n blocks in Equation (1):
H 0 n = C H T   H 0 n x + 1   | |     M H T   H n x n H 0 n = H 0 1   | |   H 2 3         H n x 2 n x 1 | |     H n x n x + 1       H n
Figure 5. Merkle hash tree (MHT).
Figure 6. Modified Merkle hash tree (MMHT).

4. Results and Discussions

The proposed system aims to provide more secure and faster execution of identity management in the blockchain. Therefore, two metrics were used in this study to evaluate the performance. The first metric is the user identity verification time, while the second is the efficiency of the encryption algorithm.
The technical comparisons between the results are based on the key size of each algorithm and the CPU processing speed for data encryption and hashing, which is based on the efficiency of hardware and software implementation and the amount of memory used to hold the data in the encryption process. The specifications of the local server representing the validator node in the blockchain are summarized in Table 1.
Table 1. Summary of local server specifications.
Different encryption algorithms were used in our proposed system model to compare the findings and assess the efficiency. Hence, this helped identify the most efficient consensus algorithm for the blockchain network and the ability to enhance identity security and integrity. Some modifications were also made to the data structure algorithm to increase its performance and overcome its complexity. Furthermore, three different transaction sizes (30, 3k, and 30k) were tested to verify the network user’s integrity performance at various transaction scalability levels. The results are produced in two stages: user encryption and blockchain hashing.

4.1. Stage 1: Signature Algorithm

This work evaluates seven encryption algorithms (a combination of RSA with five hash functions, Triple DES and AES) to provide the signature functionality. The comparison of several algorithms has the purpose of identifying the most efficient encryption algorithm for user signature in a blockchain network and the ability to enhance data security and integrity.
S i g = E n   ( P K ,   ( H 0 1   | |   H 2 3 | | H n ) )
Signatures (Sig) are generated by encrypting the private key (PK), and the final hash of the transactions data ( ( H 0 1   | |   H 2 3 | | H n ) ) , as represented in Equation (2).
We also compared the results with other works [34,35] to provide a better perspective on the performance of the compared methods. In [36], only systematic key cryptographic techniques were considered to secure cloud computing in the same encryption process. Moreover, the small transactions size was observed in [36,37]. In this paper, we consider both symmetric and asymmetric algorithms, as well as a varying number of transactions, to represent the scalability of the blockchain network. Specifically, the findings of the signature generation execution time validation for three different transactions size were considered. The evaluation was performed based on ten average simulation runs with a confidence interval of 90% to ensure the results’ high accuracy and credibility.
The results of 30, 3k, and 30k transactions shown in Table 2 and Figure 7, record the execution time in milliseconds (ms). The table is categorized into symmetric and asymmetric cryptographic keys. Meanwhile, Figure 8 compares the execution time on a logarithmic scale. It can be seen that symmetric encryption has a higher execution time compared to asymmetric encryption. From the public key group, the RSA algorithm using the MD5 hash function has the best execution time, significantly different from the other algorithms for the 30 transactions dataset.
Table 2. Comparison of signature algorithm execution time in milliseconds.
Figure 7. Signature algorithm execution time.
Figure 8. The encryption algorithm (Stage 1) and hashing algorithm (Stage 2).
However, symmetric encryptions generally have a significantly better execution time than asymmetric algorithm execution. It can be seen that the AES-128 algorithm has the lowest execution time from the smallest 30 transactions up to the largest 30k transactions. This proves that the AES-128 is a scalable algorithm that gives the best execution time in the blockchain network.

4.2. Stage 2: Blockchain Hashing Algorithm

From the MHT and MMHT design architecture shown in Figure 5 and Figure 6, the blockchain network works by adding hashing procedure to the distributed chain to validate the transactions. As a result, the total execution time is the time taken to complete the first stage En (encryption) and the second stage H (hashing) operations using either MHT or MMHT, as shown in Equation (3) and Figure 8.
E x e c u t i o n   t i m e = S t a g e   1   E n + S t a g e   2   H
Table 3 shows the results from large-scale 30k transactions using MHT, while Table 4 shows the results using MMHT. This is an extension to our previous work in [34] which studied various hash functions for MHT and MMHT blockchain networks, but did not include user integrity when using the signature. For the asymmetric encryption algorithm, RSA (MD5) integrated with SHA384 gives the best performance for the MHT algorithm as seen in Table 3, while RSA (MD5) integrated with SHA3 gives the most time optimum using MMHT. On the other hand, for the symmetric algorithm, the integration of AES-128 in Stage 1 and SHA3 in Stage 2 gives the optimum execution time over the asymmetric algorithms for both MHT and MMHT algorithms. Note that AES-128 is faster than AES-256 in execution time because of the smaller key size, but AES-256 is more robust against a brute-force attack by requiring more quantum computing power and a massive number of years to break the algorithm. However, for a blockchain network, AES-128 is more optimal in security and execution time implementation. Therefore, we highlight the execution time of AES-128 for different transactions size, as shown in Figure 9.
Table 3. Comparison of integrated signature and MHT algorithm execution time using 30k transactions dataset.
Table 4. Comparison of integrated signature and MMHT algorithm execution time using 30k transactions dataset.
Figure 9. Comparison of hashing execution time (MHT & MMHT) using AES-128 symmetric cipher algorithm with three different dataset sizes.

5. Conclusions

This work proposed a blockchain system based on identity and service providers, encryption, structure hashing algorithms, and other decentralized permissioned blockchain components. User verification and encryption in a blockchain network combined with identity management systems for IoT provide high security against any possible identity threats. A practical design of identity signatures can be effectively used in decentralized IoT blockchain networks. The design and architecture of an identity management system with different criteria are utilized to ensure user integrity and system performance. Furthermore, encryption using various algorithms based on the Merkle hash tree algorithm in both traditional and modified versions was adopted for user integrity verification check, comparing 15 different hash functions to find the optimum hash function tested in the data structure algorithm. Encryption using a symmetric AES key algorithm showed a significantly lower execution time than the asymmetric key RSA algorithm. The results showed that the AES-128 encryption and MMHT algorithm has the best execution time contribution of 36% compared with other encryption algorithms and hash function groups.

Author Contributions

Study conception and design: A.R.K., N.F.A.; data collection: A.R.K.; analysis and interpretation of results: A.R.K., N.F.A., A.A.-S., R.N.; draft manuscript preparation: A.R.K., N.F.A., A.A.-S., R.N.; funding acquisition: N.F.A., A.A.-S., R.N. All authors have read and agreed to the published version of the manuscript.

Funding

Part of this work was supported by the Malaysian Ministry of Higher Education and Universiti Kebangsaan Malaysia (Grant number GUP-2021-023).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found here: https://doi.org/10.5281/zenodo.3557461.

Acknowledgments

The authors would like to acknowledge the Ethereum dataset from [38].

Conflicts of Interest

The authors declare that they have no conflict of interest to report regarding the present study.

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