Towards a Secure Signature Scheme Based on Multimodal Biometric Technology: Application for IOT Blockchain Network

Abstract

Blockchain technology has been commonly used in the last years in numerous fields, such as transactions documenting and monitoring real assets (house, cash) or intangible assets (copyright, intellectual property). The internet of things (IoT) technology, on the other hand, has become the main driver of the fourth industrial revolution, and is currently utilized in diverse fields of industry. New approaches have been established through improving the authentication methods in the blockchain to address the constraints of scalability and protection in IoT operating environments of distributed blockchain technology by control of a private key. However, these authentication mechanisms do not consider security when applying IoT to the network, as the nature of IoT communication with numerous entities all the time in various locations increases security risks resulting in extreme asset damage. This posed many difficulties in finding harmony between security and scalability. To address this gap, the work suggested in this paper adapts multimodal biometrics to strengthen network security by extracting a private key with high entropy. Additionally, via a whitelist, the suggested scheme evaluates the security score for the IoT system with a blockchain smart contract to guarantee that highly secured applications authenticate easily and restrict compromised devices. Experimental results indicate that our system is existentially unforgeable to an efficient message attack, and therefore, decreases the expansion of infected devices to the network by up to 49 percent relative to traditional schemes

Full text

Symmetry 2020, 12, 1699; doi:10.3390/sym12101699 www.mdpi.com/journal/symmetry
Article
Towards a Secure Signature Scheme Based
on Multimodal Biometric Technology: Application
for IOT Blockchain Network
Oday A. Hassen 1, Ansam A. Abdulhussein 2, Saad M. Darwish 3,*, Zulaiha Ali Othman 4,
Sabrina Tiun 4 and Yasmin A. Lotfy 5
1 Ministry of Education, Wasit Education Directorate, Kut 52001, Iraq; odayali@uowasit.edu.iq
2 Information Technology Center, Iraqi Commission for Computers and Informatics, Baghdad 10081, Iraq;
P102907@siswa.ukm.edu.my
3 Department of Information Technology, Institute of Graduate Studies and Research, Alexandria
University, 163 Horreya Avenue, El–Shatby, Alexandria 21526, Egypt
4 Centre of Artificial Intelligence, Faculty of Information Sciences and Technology, University Kebangsaan
Malaysia, Bangi 43600 UKM, Malaysia; zao@ukm.edu.my (Z.A.O.); sabrinatiun@ukm.edu.my (S.T.)
5 Department of Computers, Faculty of Engineering, Pharos University in Alexandria,
Alexandria 21648, Egypt; yasmin.lotfy@pua.edu.eg
* Correspondence: saad.darwish@alexu.edu.eg; Tel.: +20-122-263-2369
Received: 13 September 2020; Accepted: 12 October 2020; Published: 15 October 2020
Abstract: Blockchain technology has been commonly used in the last years in numerous fields, such
as transactions documenting and monitoring real assets (house, cash) or intangible assets
(copyright, intellectual property). The internet of things (IoT) technology, on the other hand, has
become the main driver of the fourth industrial revolution, and is currently utilized in diverse fields
of industry. New approaches have been established through improving the authentication methods
in the blockchain to address the constraints of scalability and protection in IoT operating
environments of distributed blockchain technology by control of a private key. However, these
authentication mechanisms do not consider security when applying IoT to the network, as the
nature of IoT communication with numerous entities all the time in various locations increases
security risks resulting in extreme asset damage. This posed many difficulties in finding harmony
between security and scalability. To address this gap, the work suggested in this paper adapts
multimodal biometrics to strengthen network security by extracting a private key with high
entropy. Additionally, via a whitelist, the suggested scheme evaluates the security score for the IoT
system with a blockchain smart contract to guarantee that highly secured applications authenticate
easily and restrict compromised devices. Experimental results indicate that our system is
existentially unforgeable to an efficient message attack, and therefore, decreases the expansion of
infected devices to the network by up to 49 percent relative to traditional schemes.
Keywords: blockchain system; IoT; permissioned distributed ledger; bilinear pairing; fuzzy identity
based signature; multimodal biometrics; feature fusion
1. Introduction
As life shifts rapidly online, one of the problems confronting internet users is making a
transaction in an atmosphere where they cannot meet or trust each other. This eventually raises the
demand for a cost-effective, safe data transmission setting. Blockchain is a peer-to-peer network that
is cryptographically protected, irreversible, and modified only through peer agreement [1]. It is a

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successful application where peers can share values using transactions without the need for a central
authority to safeguard consumer privacy and avoid identity fraud [2]. Transaction ownership is
defined by digital keys, user identities, and digital signatures [3]. Digital signature authentication
safeguards the blockchain transaction by ensuring the transaction author has a valid private key [4].
Unfortunately, in some sensitive fields where strict authentication is needed, this strategy does not
guarantee that a transaction maker is an authorized person, as there is a possibility that an attacker
may capture the secret and produce unauthorized transactions.
Early blockchain networks are being applied to Distributed Ledger Technology (DLT), which
indicates that in a decentralized network without authorization, the users are exposed to each other,
rather than private, and thus completely untrustworthy [5]. Thus, although participants are not
willing to trust each other entirely, the network may be run on the basis of a governance model
centered on the trust between participants, for instance, a legal arrangement or a conflict settlement
system. This type of network needed participants to be known, high transaction efficiency, and low
transaction confirmation duration, privacy, and transaction secrecy. Permissioned blockchain [6]
work for a large variety of business uses, including accounting, finance, insurance, healthcare, human
resources, supply chain, and even digital music.
To define and retain a blockchain participant’s proof of ownership of some digital properties, a
blockchain is built on asymmetric cryptography [7,8] and a digital signature system; each consumer
owns a pair of private keys and a public key. This scheme includes two phases: the signing and
verification process. In the signing phase, a sender produces a transaction that contains the
cryptographic signature of the sender (hash value extracted from the transaction, then encrypts it
using its private key) and the public key of the recipient. In the verification phase, the digital signature
of the sender is checked in the previous transaction by the public key of the sender and the hash by
the same hash function as senders. If these two values are the same, the sender has already signed
the contract, and the transaction will need to be legitimate, else the transaction is rejected [9]. This
verification scheme is a key blockchain tool. In a traditional blockchain, users or membership servers
handle private keys or store them at wallets to maintain security. Private keys, however, threaten
leakage. The blockchain technology gets insecure. Figure 1 exemplifies a Blockchain transaction [10].
Figure 1. An example of blockchain transaction.
Several strategies were introduced in digital wallets [11] that attempt to force strict protection to
handle key pairs to solve the above problems. Wallet software uses the cryptographic protocol to sign
a private key transaction. The computer normally contains private keys. However, missing or

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revealing these private keys prevents a person from accessing funds [12,13]. Because possession of
every resource in the blockchain network is checked with private key, the consumer needs to safely
protect the private key. One of the interesting ways to solving this problem is to use biometric data
[14], including using one’s fingerprint, face, and iris as a private key. Since the biometrics of a
consumer is part of the human body, it may provide a safer and accessible means of connecting the
person with his private key; unlike passwords, it is not forgotten and much harder to hack than cards.
Recently, the incorporation of a fuzzy private key in biometric authentication has gained much
attention from researchers operating in this field to control vagueness in biometric details [15].
The Internet of things (IoT) offers a more efficient experience by allowing multiple devices to
connect and exchange information [16]. IoT devices gather and store different forms of information,
including frequent personal and confidential details. However, intrusion and cyber-attacks targeting
IoT devices increase every year and, like other low-power and low-performance IoT devices, find it
impossible to extend protection methods implemented for conventional PCs to IoT devices, rendering
them susceptible to cyber-attacks [17,18]. As IoT devices face such security challenges, it can quickly
leak sensitive details, trigger financial damage, and even endanger human life. To solve this dilemma,
Blockchain Technology and IoT Incorporation have attracted attention [19].
Motivated by the above-mentioned challenges, we aim in this paper to implement multimodal
biometrics technology in the blockchain network for authentication which can safely and
automatically broaden IoT products. By utilizing biometrics, the suggested model guarantees that
not just the right key but also an authenticated user is in the transaction creator. We present a multi-
modal biometric framework to minimize spoofing chance and compensate for shortcomings of
biometric unimodal systems. It fuses two attributes and gets the most unique private entropy key.
Furthermore, even though the consumer is authenticated to the network, our framework
automatically calculates the IoT security score by utilizing the whitelist, and smart contract restricts
the scalability of the infected devices while there is a low score and automatically changes the
whitelist to improve the security score, which contributes to protect IoT devices being expanded to
the network.
The proposed model relies on boosting the overall efficiency of (1) the image analysis used to
enhance image properties, identifying edges and points where texture varies, (2) the feature level
fusion used to offer quick matching speed with high precision by choosing several strong features,
(3) the fuzzy coding based on identity, which is used to distinguish key pairs, (4) the fuzzy matching
used in the transaction authorization process to match the digital signature, and (5) the whitelist used
for measuring the IoT system protection score and restricting its low score software to maintain the
security of the network.
The remainder of this article is structured as follows. Section 2 discusses several of the latest
related works. Section 3 provides a detailed description of the proposed model. The results and
discussions are given in Section 4. Finally, in Section 5, the conclusion is annotated.
2. Literature Review
The traditional protection of private blockchain keys is largely carried out in two forms, either
by encrypting keys or designing wallets on hardware or software [20]. These are cumbersome,
however, and confidentiality cannot be ensured, and all these wallets must synchronize the
blockchain, while most existing mobile devices cannot store all blocks. In 2018, Dai et al. [21]
suggested a lightweight wallet focused on Trustzone [22], a framework providing hardware-
dependent isolation, which can create a stable and consistent code environment needing high
protection. It is more compact than the hardware wallet and stronger than the software wallet.
The Internet of things has gained the most support from scholars in recent years [23]. For
example, in 2016, the authors [24] create a shared blockchain-to-IoT framework for automated smart
contract. This solution proposes a modular, stable, non-centralized authority network. The aim is not
just to validate that the right computer produced a blockchain transaction, but also that a consumer
can create a blockchain transaction for his purpose. However, verifying the user’s purpose from
automatically created blockchain transactions is difficult. In another work, in 2017, Balfanz [25]

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scanned biometric details such as fingerprints, irises, etc. in protected hardware and then triggered
the private key. By integrating such security with blockchain, the author guaranteed a secure
blockchain. However, it is important to hold a smartphone with registered biometric details and to
enter biometric information from dedicated stable hardware.
Krishna in 2019 [26] presented a novel trust model, Vriksh: the Tree of Trust (VTT), tailored for
use in IoT. This model aims to provide an embedded device-friendly entity authentication and limit
the trust peripheries. With VTT, trust trees group the identities with equal access rights in the system
using Merkle trees. The author prototyped the use of VTT with Transport Layer Security (TLS) on
raw public keys to compare the energy and resource efficiency of VTT with Public Key Infrastructure
(PKI) on an embedded platform. However, to establish VTT as an alternative to PKI, the verification
of the revocation methods for VTT and independent security reviews are essential. The PKI certificate
provides entity authentication for hyper ledger fabric.
Biswas in 2020 [27] utilized ring signature for the aim of enhancing the privacy of the
decentralization identifiers. Ring signature schemes enable the generation of anonymous signatures
where the real signer’s identity is hidden in a set of possible signers; it could be used for anonymous
membership authentication to keep the anonymity of the signer and can be publicly verifiable. Note
that the size of any ring signature must grow linearly with the size of the ring since it must list the
ring members; this is an inherent disadvantage of ring signatures as compared to group signatures
that use predefined groups. In cases like know-your-customer and anti-money laundering, ring
signature cannot be used, as the regulations must be followed, such as that participants must be
identified/identifiable and networks need to be permissioned. Blockchain’s fundamental principles
are cryptographic and cryptographic technologies that include efficient, safe, decentralized solutions.
Although several recent articles research the use-cases of blockchain in various industrial fields, such
as IoT, few studies scrutinize the cryptographic principles used in blockchain. In [28], the authors
studied and systematized all cryptographic principles already used in blockchain. They also included
potential instantiations of these blockchain principles.
Since blockchain still considers modern technologies, actual implementations can still be made
more effective and realistic. According to the above analysis, past research was mainly devoted to (1)
creating various styles of wallets, either offline or online, utilizing private key storage and wallet
backups, (2) creating modern singing and verification methods by encrypting a private key that also
does not guarantee safe authentication, (3) not discussing the differing problem of issuing a
transaction from a legitimate user and hacking a private key, and (4) not resolving the problem of
expanding the infected device to the network. However, little attention has been paid to advise new
optimal methods to merge blockchain and biometrics at the algorithm level to enhance IoT protection
and accessibility in the blockchain-based framework.
Our work allows us to construct biometrics over the existing PKI by using a fuzzy identity-based
signature. The suggested model uses multimodal biometrics to extract a secret key that varies the
secret key every time the user scans his biometric traits. A fuzzy identity-based signature simplifies
the key management procedures in case of a fuzzy biometric key in order to improve the privacy
solutions in the blockchain network.
3. Methodology
This paper presents a new paradigm that integrates multimodal biometric and blockchain
technologies in a single system focused on a fuzzy identity-based signature to ensure safe IoT
application authentication and allow blockchain extension. The suggested model takes into account
that each IoT system has security vulnerabilities and is vulnerable to installing infected applications,
thus, the devices’ security score is measured using the whitelist, which specifies the list of checked
applications and then restricts things other than the list [29]. To derive a private key, we apply a
modern multimodal biometrics-based feature level fusion of fingerprint and finger vein to obtain a
biometric identity vector. In order for the creator of the transaction to send data through their IoT
Device, they sign the piece of data with their own private biometric key to create a biometric digital
signature, and send the transaction to the blockchain network, which includes the signature and copy

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of their public key, the content and their hash value, and the transaction with an unconfirmed state.
To validate this, a specific strict authentication is implemented using the biometric public key from
the previous transition, signature, and content hash. If the proof is true, the block is applied to the
Blockchain’s public ledger, and data are sent across the network; otherwise, the block is denied. The
blockchain system’s key diagram is seen in Figure 2.
Figure 2. Block diagram of the proposed scheme.

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Despite the benefits of biometric identification, biometric details are significantly challenging to
replicate and noisy, since two biometric scans of the same characteristics are seldom equivalent.
Therefore, standard protocols cannot guarantee consistency, even when parties are utilizing mutual
biometric secrets. We use a fuzzy identity-based signature to solve this issue to sign and validate the
blockchain framework. This paper is a substantial extension of our conference paper [30]. Compared
with this small version, further details of the suggested method are presented, and a more extensive
performance evaluation is conducted. We also give a more comprehensive literature review to
introduce the background of the offered method and make the paper more self-contained. Therefore,
this version of the paper provides a more comprehensive and systematic report of the previous work.
3.1. Signature Scheme Using Fuzzy Identity
A Fuzzy Identity Based Signature (FIBS) [31–34] is used to produce and validate IoT blockchain
transactions. An FIBS uses fuzzy data as a cryptographic key, such as a fingerprint, iris, finger vein,
etc., unlike conventional digital signature schemes, which require fixed data as a key, since the
individual here can produce a different key each time they want to make a transaction. An FIBS lets
a person with identity w create a signature that can be checked with identity w’ only if w and w’ are
within a certain range. By adding biometrics to a blockchain scheme, the protection can be enhanced
through the strict checking of the transaction’s author. The fuzzy signature scheme for identification
consists of the following four steps:
- Setup (n, d): The setup algorithm takes a security parameter n and an error tolerance parameter
d as input. It generates the master key (MK) and public parameters (PP) (Public Key).
- Extract (PP, MK, ): The private key generation algorithm takes the master key MK and the user
biometric fused vector  as input. It outputs a private key associated with , denoted by .
- Sign (PP, , M): The signing algorithm takes the public parameters PP, a private key , and
a message M as input. It outputs the signature σ.
- Verify (PP, ’, M, σ): The verification algorithm takes the public parameters PP, a user biometric
fused vector ’ such that |’∩|≥ d, the message M and the corresponding signature σ as input.
It returns a bit b, where b = 1 means that the signature is valid; otherwise, the signature is not
valid.
In order for user to create and send a transaction to another user in our scheme, they have to go
through four phases: biometric key extraction, Registration, Transaction generation, and the
Verification phases. Algorithm 1 illustrates the pseudo code of our proposed model.
Algorithm 1 Pseudo Code of our Proposed Scheme
1-Biometric Key Extraction Phase
Pre-Processing
Feature Extraction
Feature Level Fusion
Return w
2-Registration Phase
Algorithm Setup (n,d)
Return PP
Algorithm Extract (PP, MK, w)
Return Kw
3-Transaction Generation Phase
Algorithm Sign (PP, Kw, H)
Return σ
4-Verification Phase
Algorithm Verify (PP, w’, H, σ)
Return True or False

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In terms of internally protecting the IoT device from being compromised due to malicious
application installed through inattention of the consumer or hackers, the suggested model calculates
the security score dependent on checking a whitelist stored in an agent inserted in a protected region
of the system. The whitelist includes all IoT-installed applications. Figure 3 shows the system
configuration for evaluating the security score for the IoT device, which is described in the following
steps [29]:
Step 1: IoT device manufacturers compose whitelist software that is installed on IoT devices.
Step 2: Device manufacturers build a smart contract comprising manufacturers’ whitelist and the
agent’s initial agent hash value (IAHV) that is embedded in an IoT device. The Whitelist Smart
Contract (WSC) records this value in the blockchain.
Step 3: The IoT device access the WSC recorded in the blockchain and verifies if the IAHV of the
agent matches the Device Agent Hash Value (DAHV) of the current whitelist installed on the
device.
Step 4: In the case of successful verification, the device is not infected nor hacked and the security
score is set to be high and vice versa.
Step 5: A Scoring Smart Contract (SSC) is created, which involves the security status of the IoT
device, which is evaluated by the agent and the device-unique identifier, and is recorded in the
blockchain.
Step 6: The SSC of the device can be inquired when the device is connected to other devices. Based
on the recording in the blockchain, the IoT device can be extended safely and quickly when
connected to other devices. The WSC collects and records in the blockchain the whitelist of each
IoT system and the IAHV from the producer. If the WSC tests by matching the IAHV with the
device’s current hash, it may give a warning message to the IoT and the vendor if they do not
fit. The whitelist recorded in the blockchain is then forwarded to the IoT device, and the Agent
uses the transmitted details and sends the list of the checked and unverified apps to the SSC [35].
Figure 4 illustrates the concept of WSC.
Figure 3. Security Score Evaluation for the internet of things (IoT) Device.

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Figure 4. Whitelist Smart Contract [35].
3.2. Biometric Key Extraction Phase
This process aims to produce biometric data w by extracting specific features from both the
fingerprint and finger vein. The proposed model implements image-enhancing strategies to increase
contrast, brightness, and noise removal. Regions of interest (ROIs) are derived for both biometric
images. Based on a unified Gabor filters frame, a fingerprint vector and a finger vein vector are
created. Then, the two vectors are decreased in dimensionality by implementing Principle
Component Analysis (PCA) to construct the private key. The reasons for the combination of
fingerprint and finger vein characteristics are that (1) the finger vein and fingerprint are two
characteristics borne by one finger and both have completely accurate biometric properties [36], (2)
ridge texture specifics dominate all biometric images, and (3) fingerprints and finger veins are
complementary in universality, precision, and protection. Figure 5 displays the biometric key
extraction graphical diagram. The method of generating the private key is as follows:
Figure 5. Schematic diagram of the fingerprint and finger vein biometric key extraction. ROI: Regions
of interest.

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3.2.1. Pre-Processing
The user fingerprint and finger vein images are converted into greyscale images. The main
reason why grayscale representations are often used for extracting descriptors instead of operating
on color images directly is that grayscale simplifies the algorithm and reduces computational
requirements. Then, the image enhancement technique is used to improve the contrast and brightness
properties of the images, followed by histogram equalization to eliminate the noise from the images
[37]. In order to reliably exploit texture details, stable ROIs corresponding to the fingerprints and
finger veins should be extracted at an early stage.
In this scenario, various methods of ROI extraction should be practiced. Core point detection
using fingerprint orientation field was used for fingerprint ROI extraction [38], measured using
gradient-based technology, and optimized neighborhood averages to produce a smoother field of
orientation. Herein, the fingerprint image is cropped into 168 × 168 pixels. For the extraction of finger
veins, inter-phalangeal joint prior to finger vein segment ROI is being used [39]. The finger vein image
is cropped into 160 × 80 pixels. Some results are shown in Figure 6.
Figure 6. ROI extraction. (Top row) ROIs of four fingerprint image samples. (Bottom Row) ROIs of
four finger vein image samples.
3.2.2. Feature Extraction
Gabor filters were commonly utilized in the spatial domain for study of texture information and
recognition functionality. Gabor filters may be divided into a true and imaginary component with
the aid of the Euler formula. The real part, also referred to as the Gabor symmetric filters, can be
calculated at the border (ridge) of the image [40], while the imaginary part, generally referred to as
the Gabor symmetric filters, can be used to detect the border [41]. In order to achieve the optimum
effects, the symmetric Gabor filters should also be built according to all styles of textures. Gabor filter-
based feature extractor is a Gabor filter bank defined by its parameters, including frequencies,
orientations, and smooth parameters of the Gaussian envelope. See [42,43] for more details. Eight
filtered images can be generated according by a two-dimensional (2D) convolution between an ROI
and Gabor filtered at eight directions. With the Average Absolute Deviation (AAD) of each block 8 ×
8/16 × 8, we can construct two features, Up and Uv, which each reflect the local features of a filtered
fingerprint/finger-venal image. The matrix Up and Uv of eight orientations are easily rearranged in a
row through two one-dimensional (1D) vectors called the fingerprint code  and finger vein
code 
.
3.2.3. Feature Level Fusion
In feature-level fusion, feature vectors must be fused into a template to improve human
recognition by integrating several features. We use the principal component analysis (PCA) feature
fusion to orthogonally turn measurements of a series of correlated variables into a collection of values
of linearly uncorrelated variables [44]. PCA is a very popular way to speed up a Machine Learning

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algorithm by extracting associated variables that do not contribute to decisions. Algorithm training
time reduces significantly with fewer features. PCA helps with overfitting by reducing the number
of features. Because we deal with a private blockchain where all members have identities, in
traditional permissioned blockchain they use public key infrastructure to generate cryptographic
certificates that are tied to organizations, network components, and end user’s applications. In our
proposed scheme, we are developing a biometric key infrastructure with a biometric certificate
authority to validate participant identities to determine specific resource rights and access to
information on the blockchain network.
3.3. Registration Phase
The user’s biometric private key  is produced from the user’s biometric data w in this process,
and its public key associated with the private key is also developed and certified, and recorded in the
blockchain network. This is a four-step phase [29,31–34]: (1) Confirming the User Identity. Biometric
Certificate Authority (BCA) confirms the identity of the user, and then obtains the biometric
information after fused into a vector  . (2) Generating Public key and Master Key. To setup the
system, first, choose  = ,   , where  is the prime order , where  is a large prime
number. From this, the value  = (, ) is calculated. Next, choose , … … . . ,  uniformly at
random from . Finally, choose y uniformly at random in , where  denotes the group
{0,1, … . . ,  − 1} under addition modulo q. (3) Generating Biometric Information Combined with a
Private Key. (4) Granting a Public Key Certificate. The Biometric Certificate Authority (BCA) issues
a Public Key Certificate (PKC) by assigning the public key to the digital signature, which is a
collection of certificate holder attributes such as a user ID (UID), expiry date, and other information.
All these features are encrypted by the BCA’s private key to invalidate the certificate. Then, the BCA
signs and publishes a PKC in the file and publishes it to the network.
3.4. Transaction Generation Phase
Herein, the sender creates a new blockchain transaction that includes the owner’s PKC (the
receiver’s PKC), content, and hash value H. Herein, the user signs the message by the hash value for
the identity  with using his private key  to create the new blockchain transaction. After that,
the biometric signature  is generated from the hash value  using his biometric information 
(fuzzy signature). The sender adds its biometric signature S to the latest blockchain transaction, thus,
this new block transaction is added to the ledger waiting for validation or rejection [44].
3.5. Transaction Verification Phase
In this step, we use the PP public parameters verification algorithm, a ′ identity so
that | ∩ ′|≥ , Hash message H, and the corresponding signature S as an input. It returns a bit b,
where b = 1 implies that the signature is correct. The verification is achieved through hierarchical
verification with two stages. (1) A transaction verifier checks the expiration date and other attributes
of the owner’s PKC from the previous blockchain transaction and verifies it using the BCA public
key. (2) The transaction verifier calculates a signature verification result. Successful verification
means that a blockchain transaction is generated using a correct private key corresponding to the
public key. Our scheme does not need to store a user’s private key in any device or cloud servers as
the user’s biometric information acts as a user’s private key [45–47].
4. Results and Discussion
No accessible database currently holds fingerprint and finger vein images for the same user. The
prepared fingerprint and finger vein database are selected from the University of Shandong
SDUMLA-HMT database [48]. We assume that the same individual has different biometric
characteristics. Fingerprint and finger vein images from 106 participants have been obtained. The
fingerprint archive contains images from both hands’ thumb finger, finger index, and middle finger.
In total, eight impressions for each of the six fingers were recorded. The finger vein database

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comprises images obtained from both hands’ index finger, middle finger, and ring finger. The set is
repeated for each finger six times in order to achieve six finger vein images per finger.
The assessment is carried out using IoT specifications: CPU: Intel Core i7 7500U Processor 2.7
GHz and Memory: 8 GB. Device type: 64-bit Business as an operating system. MATLAB has been
used to implement the biometric aspect of the key extraction. The blockchain component of the
Hyperledger Fabric [45] open-source blockchain framework was used to build and implement cross-
industry blockchain technologies and systems. In addition, in this evaluation, we use the fuzzy
identity-based signature as a digital signature algorithm [46].
4.1. Security Evaluation
In our framework, we limit the installation of malicious software on the IoT system by
automating the assessment on the whitelist and smart contract of the security score and then applying
blockchain recording. Our suggested scheme guarantees optimum scalability when the security score
is strong and scalability is limited when the security score is poor. We also carry out our tests with
the adversary’s attacks. The opponent has two common attacks to get these data. One attacks the
private key, the other forges the signature.
4.1.1. Experiment 1: Private key Attack or Leakage
In a typical Blockchain network, the private key is the critical value that decides the ownership
of the transaction especially if the transaction is serious. Thus, this experiment is run to analyze the
robustness when the user’s private key has been leaked from an IoT device due to IoT device theft,
cyber-attack, etc. Multimodal biometrics based on feature level fusion algorithm are implemented in
this study in order to find the most important features to extract a unique private key that helps to
make our framework more robust and secure. In the first set of experiments, we analyze the security
of our proposed scheme and confirm their effectiveness against common attacks in the blockchain
system; additionally, we compare the security level against three signature schemes. In the first
experiment, we examined the security and efficacy of our proposed scheme against typical attacks in
the blockchain environment and compared the safety level with three signature schemes: the
conventional private key based signature scheme (PKSS), the fuzzy key signature scheme based on
unimodal biometrics (FKSSU), and our proposed fuzzy identity key signature scheme based on
multimodal biometrics (FIKSSM). From Table 1, it can be seen that the proposed model is more secure
than the previous methods when it comes to a private key leakage.
Table 1. Security Level Comparison. PKSS: conventional private key based signature scheme; FKSSU:
fuzzy key signature scheme based on unimodal biometrics; FIKSSM: fuzzy identity key signature
scheme based on multimodal biometrics.
Signature Approach Leakage of Private Key
Digital Signature Forgery
PKSS Low Middle
FKSSU Middle Middle-High
FIKSSM High High
In PKSS, the IoT device, a cloud service, or wallet controls the long-term private key. Therefore,
an adversary threatens to capture the private key by producing a digital signature and making an
illicit blockchain transaction. Losing the secret key involves losing the asset’s possession
permanently, since the private key cannot be restored. This leaves PKSS susceptible to security
breaches or cyber threats and offers inadequate security in today’s connected and data-driven
environment. In FKSSU, however, the private key is not handled in the IoT device. The user’s private
key arises from biometric knowledge. Thus, FKSSU finds a stable scheme based on just one biometric
trait. FKSSU has many challenges in capturing clean data, including noisy data concerns, inter-class
variations, spoof attacks, and unreasonable error rates [48]. The FKSSU is not absolutely safe against
this attack. In our FIKSSM, the IoT device does not manage the private key like FKSSU. However, we
use the user’s fingerprint and finger knuckle print to derive a unique private key; using multimodal

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biometrics increases the amount of data evaluated, helps verify the match accurately, and makes
spoofing exponentially more difficult for the opponent, since incorporating several modalities allows
identifying and utilizing all the biometric data required to spoof the algorithm harder. This allows
our device to be more stable and efficient and can prevent spoofing attacks.
4.1.2. Experiment 2: Forgery of Digital Signatures
This threat is to fake a digital signature in order to create an illicit blockchain transaction and to
effectively validate a digital signature. It is possible to manage this threat if we adopt a safe algorithm
to create key pairs. When a stable signature algorithm is used in PKSS and FKSSU, which is difficult
to forge, these signature schemes are protected. Table 1 indicates that the proposed model is safer in
digital signature forgery than the previous approaches. We use a stable fuzzy identity signature
algorithm in the FIKSSM, which was proved safe under EUF-CMA (Existential Unforgeability against
Chosen Adaptive Message Attacks) [31]. The likelihood of adversaries generating a legitimate
signature for a message under a new private key is insignificant in the EUF-CMA model.
4.1.3. Experiment 3: Security Score Evaluation
We have deployed a simulation network of 200 IoT device nodes randomly arranged at a known
location to test the efficiency of our proposed model in terms of security score with the number of
Unverified Software. The security score is set to all 200 nodes and the agent and the smart contract
are believed to be registered in the blockchain. Based on the whitelist upgrade, we determine the
security score. All verified software exists in the whitelist; if unverified software is installed on the
IoT device, it is said to be infected with a virus and the security score will automatically drop
according to the number of unverified software programs. Additionally, when viruses are removed
by the whitelist update, it is set to improve scalability by increasing the security score. Table 2
illustrates the connection range comparison of our proposed scheme against the FKSSU signature
scheme. The lower the score is, the more restriction to the IoT device to extend to other devices in the
network. That is why our proposed system offers different connection ranges depending on the
security score of the device. The other comparative scheme is set to extend the IoT device to all the
devices within the same connection range regardless of the security score of the device.
Table 2. Security Score Evaluation.
Number of
Unverified Software
Security
Score
Connection Range of
Comparative Signature Scheme
Connection Range of Our
Proposed Signature Scheme
0 100–81 4 hop 4 hop
1 80–61 4 hop 3 hop
2 60–41 4 hop 2 hop
3 40–0 4 hop 1 hop
4.2. Performance Evaluation
4.2.1. Experiment 4: Throughput
For all of the experiments below, the average end-to-end latency and the average throughput
are measured to get the ideal results by firstly assuming an ideal network, then assuming infected
devices in the network, and next measuring the vulnerability of the total network. Throughput is
defined as the result of the successfully transmitted packets divided by the packets’ transmission time
[49]. As shown in Figure 7, the number of transmitted packets is reduced when the update period
becomes longer, resulting in poor network performance, because network scalability is restricted
when the update period is set to be excessively long, resulting in a decrease in throughput. On the
other hand, the number of transmitted packets is improved only when the update period increases
to a certain level. Therefore, based on the simulation results, we will set the basic update period to 10
times as the highest throughput was achieved when 10 connection failures were accumulated, after
which the whitelist was updated. As shown in Figure 8, we set the update period to be 10 times to

Symmetry 2020, 12, 1699 13 of 18
get the highest throughput; in our FIKSSM proposed model, the throughput results measured for a
network size of 200 IoT devices achieved a higher throughput performance within the available
network resource compared to the other signature schemes, which is a desirable goal in any network.
Figure 7. Throughput results when changing the update period for the proposed model.
Figure 8. Comparison results of throughput.
4.2.2. Experiment 5: Scalability
Scalability is defined as the ability of an IoT device to work gracefully without any undue delay
and non-productive resource consumption. It is the result of dividing the number of connected IoT
devices by the number of physically available connections. As shown in Figure 9, in our FIKSSM
proposed model, the scalability is low at the start of the simulation time compared to other signature
schemes PKSS and FKSSU; the IoT devices with low-security scores are restricted from extension to
the network, as they contain malicious software, resulting in a continuous whitelist update to remove
the unverified software. After a certain period of time, the security score increased and restricted
devices are able to extend to the network. It took 0.19 s for our proposed scheme to extend the devices’
scalability to 90% and above.

Symmetry 2020, 12, 1699 14 of 18
Figure 9. Comparison results of scalability.
4.2.3. Experiment 6: Vulnerability
Vulnerability in an IoT system is described as the faults or vulnerabilities that expose it to threats.
It comes from dividing the number of infected devices by the number of devices in the network. In
Figure 10, the horizontal axis reflects the simulated time and, through the relation to other peripheral
malicious appliances, the vertical axis represents the amount of infected IoT computers. In PKSS,
malicious devices are explicitly connected to all IoT devices in the network, which renders the scheme
susceptible to a Distributed Denial of Service (DDOS) attack, in which an intruder uses many infected
IoT devices to overload the target node. The FKSSU is still not adequately confident, as the security
score of the device when applied to the internet, which renders the device susceptible to malicious
applications installed by the user’s carelessness or by the hacker, depends only on the security of the
user authentication and verification with a unimodal biometric key. Our suggested FIKSSM scheme
decreases the number of machines attached to malicious devices to 4%. Compared to PKSS, the
number of malicious devices has decreased to 49%. As the link depends on the security level and the
agent, and the whitelist continuously checks the software, there is a decrease in the number of
malicious devices after the whitelist update.
4.2.4. Experiment 7: Complexity
Herein, the objective is to evaluate the execution factors that influence the complexity of the
suggested model. We execute different signature methods and evaluate them in terms of the size of
files and processing time. First, we evaluate the file size of the Private Key, Public Key, Public Key
Certificate, and the signature in a blockchain transaction. A Private Key includes a fused vector form
and a fingerprint and a finger vein pattern, and the file size of a Private Key is 10 Kbyte. This file size
is larger than a traditional private key. However, 10 Kbyte is small enough for practical use. The
public key includes the public parameters assumed by our scheme and the file size is 1 Kbyte. The
file sizes of a public key certificate and signature are the same in all methods, and they are 1 Kbyte
and 71 bytes, respectively.
As revealed from Table 3, the processing time of the public key generation is executed one time
in initial user registration. Thus, the processing time of 400 ms is fast enough compared to the one
used by unimodal biometrics which is 499 ms. We perform signature generation every blockchain
transaction generation. The processing time of signature generation is in the PKSS is 78 ms. In the
FKSSU, the processing time of signature generation is 1306 ms; this is slower than the PKSS and the
FIKSSM, whereas in FIKSSM, the processing time is 74 ms, which is significantly faster. We perform
signature verification every blockchain transaction verification. The processing time of signature
verification in the FIKSSM is 60 ms, which is faster compared to other blockchain procedures. In this

Symmetry 2020, 12, 1699 15 of 18
way, you can see that our proposed scheme achieves practical file size and processing time. Therefore,
we can use our scheme for a practical IoT blockchain system.
Figure 10. Comparison Results of Vulnerability.
Table 3. Implementation comparative results in terms of file size and processing time.
Execution Factors Algorithm PKSS FKSSU FIKSSM
File Size
Private Key 1 Kbyte 10 Kbyte 10 Kbyte
Public Key 1 Kbyte 1 Kbyte 1 Kbyte
Public key Certificate 1 Kbyte 1 Kbyte 1 Kbyte
Signature in a Blockchain Transaction 71 byte 71 byte 71 byte
Processing Time
Public key Generation 300 ms 499 ms 400 ms
Signature Generation 78 ms 1306 ms 74 ms
Signature Verification 70 ms 70 ms 60 ms
5. Conclusions and Future Work
In this work, we present a new signature scheme that tackles security and scalability concerns
in IoT devices focused on blockchain technologies and multimodal biometrics. The suggested model
safely expands the network communication of IoT devices and guarantees that the individual who
creates the blockchain transaction is not a fraud. The model suggested increased security in two steps.
One is to use multimodal biometric as a private key to authenticate and validate a blockchain
transaction using the fuzzy identity signature. Two of them include the implementation of whitelist
applications on IoT, and then a smart contract to record all installed software in the blockchain. A list
of available software is recorded in the IoT manufacture, and when the IoT devices are used, their
security score is evaluated by checking the number of unverified software programs on the whitelist
with the manufactured list. This restricts the infected IoT devices; by automatically updating the
whitelist, unverified software programs are removed, and the security score is raised, which leads to
adding IoT devices to the network. Experiments show that our proposed model reduces the addition
of infected devices to the network up to 49% compared to the conventional schemes. The proposed
scheme practically achieves high performance in terms of security against spoofing and signature
forgery and provide high throughput and low latency. Future work may include (1) Enhancing the
biometric authentication accuracy by applying multiple biometric traits; it is expected that it will
extract a wider range of information and this leads to a better result, (2) different approaches for
feature level fusion and extraction of biometric for private key extraction, and (3) extend the number
of peers in the blockchain network to accurately test the scalability of the permissioned network, and
its effects on performance.

Symmetry 2020, 12, 1699 16 of 18
Author Contributions: Conceptualization, S.M.D. and O.A.H., and A.A.A.; Methodology, S.M.D., O.A.H.,
Y.A.L. and Z.A.O.; Software, O.A.H., A.A.A., and Y.A.L.; Validation, S.M.D., A.A.A., O.A.H., and Z.A.O.; Formal
analysis, S.M.D., A.A.A. and O.A.H.; Investigation, S.M.D., Z.A.O., O.A.H., and S.T.; Resources, O.A.H., A.A.A.,
Y.A.L. and S.T.; Data curation, O.A.H., A.A.A. and S.T.; Writing—original draft preparation, S.M.D., A.A.A., and
O.A.H.; Writing—review and editing, S.M.D.,O.A.H. and Z.A.O.; Visualization, O.A.H., A.A.A., Y.A.L. and S.T.;
Supervision, S.M.D., Z.A.O. and A.A.A. All authors have read and agreed to the published version of the
manuscript.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflicts of interest.
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