Radius divergence optimized round hash index blockchain-based transaction-secured unified data in smart environment

Abstract

In the guise of numerous mobile gadgets and IoT devices associated to the Internet a huge amount of data called, unified data structure is generated in different forms. With the intention of coping with the coercion of storing and assessing tremendous amounts of unified data both locally and globally, data owners generally store them in cloud servers. However, the data owner will lose control of this unified data and it will be at the fear of being vandalized with and deleted. To address on these issues, in this article, a Radius Divergence-optimized and Round Hash Index Blockchain-based (RD-RHIB) transaction-secured unified data is proposed. The RD-RHIB transaction-secured unified data model is split into two sections, namely optimization and transaction security. First, Shannon Information Radius Divergence-based Unified Storage Optimization is applied to the unified stored data to ensure optimal unified data storage for further analysis. Second, Round Hash Index Asymmetric Key Blockchain-based transaction security model is designed. To this end, unification employs blockchain ledger for efficiently transmitted access permissions that grant exchange of data instead of the transfer of data itself. Followed by which complicated mathematical problems are to be solved to group validated transactions into blocks and include them into blockchain ledger. Upon successful meeting of the conditions, transaction is said to be executed and recorded on the blockchain. A suite of experiments are validated and analyzed to verify the proposed RD-RHIB method’s service availability, reliability and scalability. According to the results of the performance analysis, our RD-RHIB method has the highest data confidentiality with an improvement of 38%, data integrity improvement of 28% when compared to other conventional methods.

Introduction

Inscribing systems security by design is referred to as a notable systems engineering (SE) challenge that necessitates efficient and meticulous tools. Sophisticated systems progressively depend on digital technology to carry out an operation. Such technology necessitates software elements, configurable or customizable pieces and communication frontiers that may be utilized for coming to terms mission by influencing characteristics of its integrity, confidentiality and accessibility. Hence security engineering (SE) is the branch of knowledge perturbed with the representation of trustworthy and secure systems with the purpose of circumventing those risks. Nevertheless, the fusion of significant information into lifecycle of SE is not inconsequential as it is featured by verbose directions and has no pragmatic threat-aware systems design.

Threat and Risk Assessment for Design of Engineered Systems (TRADES) was designed in [1] by introducing a centralizing model-based technique to address the systems’ security aspects. Primary idealist users of the Internet perceived it as a technology that would underpin independence and autonomy. As a result, both security and privacy characteristics are frequently not the principal point of convergence for providers of unified communication (UC) plan of actions. Several plans of actions depend on closed-source code, and their facilities are only services and are only comprehensible by means of public clouds. Owing to this verification and validation of end-to-end encryption and key management are not possible for users but have to trust the claims made by the platform provider.

A unified framework that improves the efficiency of communication with communication compression employing federated learning, called SoteriaFL, was proposed in [2]. Initially, both compression operators and local differential privacy were employed for performing compression with respect to differentially-private stochastic gradient descent. Followed by which, private federated learning that employs family of local gradient estimators like stochastic variance-reduced gradient models was employed to minimize computational complexity.

To address both on the security aspects and use all those datasets in isolated places, a unified and privacy preservation framework was designed in [3] employing AI and multi-party computation. With this type of framework resulted in minimizing the training time along with the improvement in security, nevertheless, the probability objective frequently results in frequent and dull outputs and malfunctions to make the most of the serviceable apprehension from negative instances. In [4] a unified multimodal model with unlikelihood training was presented to handle this issue. Yet another security and privacy mechanism for unified represented was investigated in [5].

Related works

With strict rules and regulations of data privacy and data security, traditional machine learning techniques employing centralized datasets is facing with immense amount of challenges making artificial intelligence (AI) impossible in several mission critical and data-sensitive applications. At the same time, immense dataset is dispersed in an isolated fashion in several business establishments, different sections of organization. Also with the scattered nature of dataset are underused.

Over the last decade, characterization of large datasets has become a major issue as far as many scientific disciplines are concerned. Several techniques for ordering data and network visualization achieved this objective. However, there was no fundamental theoretical framework associating these issues as far as security and privacy are concerned.

A unified deep learning approach was proposed in [6] for smart grid environments. With the aid of neural network, distributed network protocol attacks were addressed with high accuracy and true positive rate. Anomalies were detected and classified into several types. However, the loss function involved in the design was not focused. Yet another unified framework employing Laplacian regularization was presented in [7] to address both convex and non-convex loss function. Communication-centralized algorithm named FedU for handling the federated multitask learning (FMTL). The learning performance was enhanced. But, the data confidentiality was not considered.

An optimized storage method employing blockchain was designed in [8]. Also, for the sake of accurate classification extreme learning machine was employed therefore ensuring both high accuracy and also optimized storage. Five features were discussed for estimating the popularity of a block. Objective feature of a block, historical popularity and storage requirements were included. However, it failed to consider data sharing between stakeholders.

Data sharing is vital in smart construction to enhance efficiency of various activities on construction sites. A dual pronged design method employing unified framework work data sharing was presented in [9]. With this type of framework, training time was reduced considerably. However, the security was not achieved.

To ensure security and audit, a method employing ontological representation was designed in [10]. The maintenance for multi-model data has become a requirement for most of the prevailing data base management systems. But, the process from a conceptual schema to logical multi-model schema of a specific database management system (DBMS) is however not found to be effortless.

In [11], transaction-based security while communicating between devices was designed. An elaborate survey of transaction security via federated learning for multimodal data was investigated in [12]. Yet another multi-model data algorithm was presented in [13]. Category theory was applied to perform mapping between multi-model data and categorical data representation for performing mutual type of transformations between devices. Dissimilar data management tasks were investigated. However, it failed to focus on deep learning.

A comprehensive review of employment of multimodal using deep learning in the field of medicine was presented in [14]. Long-term health monitoring, treatment planning and disease prevention were investigated. But, the blockchain was not employed to enhance the security.

The study examined on blockchain using secured android data storage method in [15] to both maintain security transparency and reliability into account. A survey of providing mechanisms for security and privacy using blockchain was investigated in [16]. An extensive amount of log data is said to be brought about during daily railways operation. But, the railway traffic log data are stockpiled in unconstrained department that includes highly confidential data. Hence, is becomes very significant to ensure safe sharing of log data and the provide privacy and confidentiality of the data.

In [17] a management specification for data sharing security in smart mining was presented. The data quality was ensured through data sharing. But, the data integrity was not enhanced. A blockchain-based data sharing model employing evolutionary mechanism was designed in [18] therefore ensuring stability to a greater extent.

Yet another government data sharing protocol employing generalized blockchain was presented in [19]. By this type of design, both the communication time and communication overhead were reduced significantly. A survey of security mechanisms to ensure authorization was investigated in [20]. However, the end-to-end delay was not reduced.

Blockchain-based authentication method (BCAuthEN) was developed in [21] for secure user biometrics and passwords. BCAuthEN minimizes communication overhead and increases throughput. But, the reliability was not enhanced. Blockchain-based secure mutual authentication scheme was employed in [22] with higher security and privacy. However, the data integrity was not enhanced.

The study discussed on IoTChain model in [23] for offering end-to-end encryption and fine-grained access control. However, the service availability was not considered. The security was ensured in [24] by blockchain-based access control model named SSX-EHRs. Blockchain-based IoT systems were designed in [25] with higher security. Modified Merkle Hash tree (MMHT) authentication scheme was applied for minimizing the validation execution time. But, the optimizing was not performed with lesser delay.

In all these studies, the optimization and blockchain-based methods yielded better performance upon comparison to the traditional methods for providing transaction security in smart environment. Hence, a novel method has to be applied to ensure transaction security through blockchain with optimization techniques and extend and improve the current mechanisms.

Research gap

Unified communication (UC) is employed for video conferencing, audio conferencing and instant messaging. Blockchain is a talented technology for distributed computing infrastructure network by using cryptographic signatures and smart contracts that makes it difficult for hackers to intrude into the systems. Blockchain’s distributed nature has challenging to handle large volumes of data and transactions efficiently. Several blockchain methods were developed for unified data communication. But, the security and privacy considerations have been neglected. Also, the verification and validation of end-to-end encryption and key management were unable to consider. For addressing this gap, novel blockchain-based transaction-secured data communication is developed while maintaining security. Cryptographic and consensus mechanisms of blockchain are employed to verify and validate data integrity for ensuring secure communication. Key management, key generation, encryption, verifying authentication and validation for communication are crucial to protect the confidentiality of the transmitted data.

Motivation and contributions

Though sizeable amount of research works focused on the accuracy and timeliness aspects, but did not concentrated on the data confidentiality and data integrity part. Also, certain literatures though paid attention to integrity of data but compromised on the throughput rate in ensuring transaction security for smart environment. In order to overcome the existing issues, a novel Radius Divergence-optimized and Round Hash Index Blockchain-based (RD-RHIB) transaction-secured unified data in smart environment is introduced with the following novel contributions.

• To improve the data confidentiality and data integrity rate with minimal training time, the RD-RHIB method is designed based on two major processes namely unified data storage optimization and blockchain-based transaction security.

• First, Shannon Information Radius Divergence-based Unified Storage Optimization is proposed in the RD-RHIB method that with unified stored data performs Jensen–Shannon symmetric divergence and relative entropy for sampling from a population for further processing from the raw dataset. The hyper graph is employed in our work for satisfying unified representation and is applied for maximizing throughput and packet delivery ratio in an extensive manner.

• To improve service availability, reliability and scalability, RD-RHIB method uses the relative entropy value with optimal representation, while having only a strongly suppressed amount of false positive connection.

• To improve data confidentiality and data integrity rate, the proposed Round Hash Index Asymmetric Key Blockchain-based transaction security algorithm is employed for analyzing the testing and training features. Then the Round Hash Index Asymmetric Key Generation function with the aid of password-analogous data ensures smooth and secured connection between devices in a transaction efficient manner.

• Finally, comprehensive experimental assessment is carried out with seven distinct types of performance metrics like, data confidentiality, data integrity, throughput, packet delivery ratio, service availability, reliability, scalability to illustrate the proposed RD-RHIB method over traditional methods.

Organization of the work

The rest of the paper is arranged into distinct sections as follows. Section 2 reviews the related works in the area of optimization techniques, blockchain model for transaction security in smart environment. A detailed representation of the proposed RD-RHIB method using figurative representation and pseudocode is detailed in Sect. 3. In Sect. 4, the experimental settings are described followed by detailed discussion on the performance results of the proposed and traditional methods with numerous performance metrics in Sect. 5. Finally, Sect. 6 concludes the paper.

Methodology

As far as unification or unified stored data is concerned, both bitcoin-based blockchains and Ethereum-based smart contracts have surfaced a smooth and robust process. Cooperatively, they provided with a novel potentiality to secure sensitive data employing cryptography mechanism while controlling the competence of smart contracts to automatically execute exchanges of said unified data. Nevertheless, prior to the appearance of blockchain technology, this global standardization has been very laborious and cumbersome to perceive in general. With the inception of blockchain technology has unfastened doors that were never before feasible. As far as the unification process is concerned, smart contracts play a key role. Also the access of data is carried out not entirely by terms and conditions but based on the hard-coded permissions in smart contracts that is highly laborious to decipher. This utilization of smart contracts performs twofold characters, assisting both business establishments and users. On the one hand, business establishments have rigid assurances that they will acquire the data they are purchasing in the absence of a third party to mediocre the trust association between customer and dealer. On the other hand, customers make the decisions of their data with a full view of all permissions to grant or revoke access to their data.

Shannon Information Radius Divergence-based Unified Storage Optimization

Despite unified stored data in data management server can be used for transacting between users; by optimizing unified data, service availability, reliability and scalability can be improved to a greater extent. Here, by using Shannon Information Radius Divergence-based Unified Storage Optimization model communication between users can be discarded that is found to be unwanted on any control flow path (i.e., channel) and consecutively reducing the number of processors to which data must be communicated. Figure 1 shows the structure of Shannon Information Radius Divergence-based Unified Storage Optimization model.

[See PDF for image]

Fig. 1

Structure of Shannon Information Radius Divergence-based Unified Storage Optimization model

As illustrated in the above figure, let us consider a symmetric adjacency matrix, ‘$X$’ having probabilistic entries ‘$X_{\mathit{ij}}\ge 0$’ then the objective here first remains in identifying the optimal representation in terms of another matrix, ‘$Y$’ having the same size. The objective behind the design of Shannon Information Radius Divergence-based Unified Storage Optimization model is that a representation is formulated in such a manner which is hardest to be differentiated from the input matrix via relative entropy. Let us further consider a set ‘${\text{MD}}_{+}^1\left(X\right)$’ of probability distributions where ‘$X$’ is a set including both the acceleration-based data and gyroscope-based data. Then, Jensen–Shannon symmetric divergence (JSD) divergence ‘$\text{Div}\left(X,|,|,Y\right)$’ is mathematically formulated as given below.

1

$$\text{JSD}\left(X||Y\right)=\frac{1}{2}\text{Div}\left(X||\text{MD}\right)+\frac{1}{2}\text{Div}\left(Y||\text{MD}\right)$$

From the above Eq. (1), ‘$\text{MD}=\frac{1}{2}\left(X,+,Y\right)$’ form the mixture distribution of ‘$X$’ and ‘$Y$’, respectively. By employing this mixture distribution make certain the appropriate normalizations of the probability distributions. Despite ‘$\text{Div}\left(X,|,|,Y\right)$’ is not a metric and not symmetric in ‘$X$’ and ‘$Y$,’ it is a significant and extensively related measure of statistical remoteness, evaluating the distinctiveness of ‘$Y$’ from ‘$X$.’ Also hyper graph ‘$H$’ is employed in our work for satisfying unified representation theory that with hyper edges (i.e., sensor devices) connect to a subset of nodes (i.e., activities) rather than two nodes. The highest representation is arrived at upon the relative entropy value reaching to ‘0’ achieved, and our goal is to obtain a ‘$Y_{}^{\ast }$’ representation satisfying the constraints by allowing for the comparison of more than two probability distributions as given below.

2

$$Y_{}^{\ast }={\text{argmin}}_Y\text{Div}\left(X||Y\right)$$

From the above Eq. (2), the relative entropy value result is arrived based on the satisfaction of the above constraints with respect to more than two probability distributions as given below.

3

$${\text{JSD}}_{w_1,w_2,\dots ..,w_n}\left({\text{Prob}}_1,{\text{Prob}}_2,\dots ,{\text{Prob}}_n\right)=\sum _i{w}_i\text{Div}\left({\text{Prob}}_i||\text{MD}\right)$$

4

$$=H\left(\text{MD}\right)-\sum _{i=1}^n{w}_{i}H\left({\text{Prob}}_i\right)$$

From the above Eqs. (3) and (4) ‘$\text{MD}={\sum }_{i=1}^n{\pi }_i{X}_i$’ and ‘$w_1,w_2,\cdots ..,w_n$’ represents the weights employed for the probability distribution ‘${\text{Prob}}_1,{\text{Prob}}_2,\cdots ,{\text{Prob}}_n$’ and ‘$H\left({\text{Prob}}_i\right)$’ representing the Shanon entropy for probability distribution ‘$\text{Prob}$.’ Then, the optimized value for the two probability distributions as given above is mathematically formulated as given below.

5

$${\text{Prob}}_1=X;{\text{Prob}}_2=Y;w_1=w_2=\frac{1}{2}$$

This density-preserving characteristics result to a notable computational enhancement for sparse networks. This is due to the reason that there is no requirement to construct a denser representation matrix, than the input matrix if we keep track of the relative entropy value ‘$Y_{}^{\ast }$’ normalization into account. Finally, for those two probability distributions ‘$X$,’ ‘$Y$’ the normalized distribution is bounded as given below.

6

$$\text{JSD}=H\left(M,D\right)-\frac{1}{2}\left[H,\left(X\right),H,\left(Y\right)\right]$$

From the above equation results, hence in the optimal representation of a network all the connected network elements are connected, while having only a strongly suppressed amount of false positive connections. The pseudocode representation of Shannon Information Radius Divergence-based Unified Storage Optimization is given below.

As given in the above algorithm with the objective of improving service availability, reliability and scalability while performing transaction, the unified stored data should be optimized. With this objective, the unified stored data in data management server have to be in optimized format. For that purpose, Shannon Information Radius Divergence is applied to measure similarity between two probability distributions (i.e., sensor devices with respect to activities). Next, two distribution cases are analyzed by measuring relative entropy value to arrive at optimal unified stored data representation.

Round Hash Index Asymmetric Key Blockchain-based transaction security

Owing to the reason that the unified data representation is not free from being hacked by malicious users while performing transaction, trust between devices and the device management server (DMS) is established by means of a mathematical model. In this section, a decentralized authentication platform using the distributed ledger based on Round Hash Index Asymmetric Key Generation function and Ethereum Blockchain is designed and analyzed with respect to its security features. To start with a great number of transactions (i.e., subject’s information) are grouped simultaneously to form a block. Data management servers (DMS) verify that transactions within the block respects defined rules on Round Hash Index Asymmetric Key Generation function. Followed by which the added block are validated by the DMS via consensus algorithm. Finally, verified transactions are stored in block. Ethereum blockchain smart contracts based on Round Hash Index Asymmetric Key Generation function have control of the laws and logic of authentication. A secure key generation function is employed to ensure a secure session and implements a Round Hash Index Asymmetric Key Generation function, so the DMS does not store any password-analogous data. As a result, a probable trade-off of DMS data does not result in security breach due to the reason that the malicious users cannot imitate devices. The Round Hash Index Asymmetric Key Generation function shares a private key between two parties (i.e., devices and DMS). Also a public key is extracted from a random number generated by both the devices and the DMS separately during each transaction session. Here, the public keys are stored in a decentralized manner whereas the private keys are stored inside the devices themselves. Also password-analogous data are interchanged while performing the authentication process, our proposed Round Hash Index Asymmetric Key Generation function satisfies the authentication encryption requirement.

The user or the device authenticates his Ethereum wallet address via smart contract. The input samples or the subject’s information are obtained in real time during the access of data. Figure 2 shows the structure of Round Hash Index Asymmetric Key Blockchain-based transaction security model.

[See PDF for image]

Fig. 2

Structure of Round Hash Index Asymmetric Key Blockchain-based transaction Security

As illustrated in the above figure, four steps are involved in the design of Round Hash Index Asymmetric Key Blockchain-based transaction security model. First, with the optimal unified data obtained as input, the unified structure is subjected to asymmetric key generation using Round Hash Index Asymmetric Key Generation function, followed by which block generation is made according to time interval function. Third, authentication verification is made using smart contract. Finally, validation is performed to ensure transaction security.

Round Hash Index Asymmetric Key Generation

In the blockchain network, each node or device has a unique node or device ID in the network. In addition, each node or device also has a key pair used for identity authentication and each device entering the network must generate its own key pair (public key and private key). This is done in our work by employing Round Hash Index Asymmetric Key Generation function. Each device in the blockchain network maintains a list containing the device ID and public key of all other devices. The device newly entering the blockchain network announces its public key via message broadcasting. During transaction, the DMS utilizes the private key to digitally sign data. Upon reception of data, other devices utilize the public key of the DMS to verify the data ensuring that the data is not contaminated. As illustrated in the above figure initially, the device ‘$D$’ computes the password verifier for each device. This is mathematically formulated as given below.

7

$$P_{\text{Ver}}=SHA-256\left(R,N,\left|{\text{D}}_{\text{id}}\right|,{\mathit{PWD}}_D\right)$$

From the above Eq. (7), ‘$|$’ refers to concatenation. The devices here communicate with the data management server (DMS) via unique device identification ‘$D_{\text{id}}$’ and password verified ‘$P_{\text{Ver}}$,’ respectively. The DMS then computes Round Hash Index ‘$\mathit{RH}$’ for each device ‘$D$’ using ‘$D_{\text{id}}$.’ For each OT device, it stores the values ‘$\left(D_{\text{id}},,,R,H,,,P_{\text{Ver}}\right)$’ in a cache. This is mathematically represented as given below.

8

$$\text{D}=\left({\text{D}}_{\text{id}},,,\text{RH},,,P_{\text{Ver}}\right)\to \text{Cache}$$

When the device being scaled out, previous Round Hash Index values ‘$\mathit{RH}$’ are converted to new Round Hash Index values and updates the cache subsequently. Followed by password establishment, asymmetric key is shared from a random number generated by both the devices independently during each data exchange session. The process of authentication is said to be initiated with a message from a device as given below.

9

$$D_i\to {\text{Cache}}_i:D_{\text{id}},PubK\left(D_i\right)={\mathit{SKey}}_{\text{OTP}}^{D_i}$$

The ‘${\text{Cache}}_i$’ diverts the analogous cache information, together with ‘$PubK\left(D_i\right)$’ to ‘$D_j$,’ that will serve for ‘$D_i$’ via one time password session key ‘${\mathit{SKey}}_{\mathit{OTP}}^{D_i}$’ for device ‘$D_i$.’ For which the ‘$D_j$’ respond as given below.

10

$$D_j\to D_i:RH,PubK\left(D_j\right)=P_{\mathit{Ver}}{+SKey}_{\mathit{OTP}}^{D_j}$$

From the above Eq. (10), ‘$D_i$’ has the analogous ‘$\mathit{RH}$’ value and is generated via the one time password session key ‘${\mathit{SKey}}_{\mathit{OTP}}^{D_j}$’ for device ‘$D_j$,’ respectively. Now, both ‘$D_i$’ and ‘$D_j$’ obtain the publicly disclosed random number as given below.

11

$$D_i,D_j:RN=SHA-256\left(P,u,b,K,\left(D_i\right),|PubK,\left(D_j\right)\right)$$

Followed by the above key computation as given in Eq. (11) obtained via public ephemeral values corresponding to two devices ‘$PubK\left(D_i\right)$,’ ‘$PubK\left(D_j\right)$,’ the shared session key employing Asymmetric Key Generation function by devices ‘$D_i$’ and ‘$D_j$’ is mathematically stated as given below.

12

$${\left(g^{{\mathit{RN}}_j}\right)}^{{\mathit{RN}}_i}=SEncK=g^{{\mathit{RN}}_j{\mathit{RN}}_i}ofG$$

13

$${\left(g^{{\mathit{RN}}_i}\right)}^{{\mathit{RN}}_j}=SEncK=g^{{\mathit{RN}}_i{\mathit{RN}}_j}ofG$$

From the above Eqs. (12) and (13), a random number ‘${\mathit{RN}}_i$’ is picked by the device ‘$D_i$’ and random number ‘${\mathit{RN}}_j$’ is picked by device ‘$D_j$,’ respectively, to obtain session encryption key ‘$\mathit{SEncK}$.’

Time interval-based block generation

Followed by keyword generation, the second process involved in ensuring transaction security through blockchain is the generation of block in the blockchain network. Figure 3 given below illustrates the transaction block chain structure with each block consisting of two parts, the block header and the block body.

[See PDF for image]

Fig. 3

Structure of time interval-based block generation

As illustrated in the above figure, the block header on one hand stores Merkle root, previous block hash, timestamp, Nonce and other data whereas on the other hand, the block body stores all device data. Also to reduce heavy computational and bandwidth burden in our work, a time interval-based data writing is employed that sets a time interval ‘$\mathit{TI}$’ for each subject’s device information that is written to the blockchain. This time interval-based data writing is modeled in such a manner that the initialized time interval is greater than the time consumed in sending the subject’s device information to the DMS.

Upon successful generation of blocks, smart contract-based password authentication verification is designed. To start with the user or device authenticates its Ethereum wallet address to the smart contract by means of unique ID. The input samples or the device’s samples are taken in real time during the data access. Followed by which with the above establishment of password and keyword computation, password authentication is performed as given below using smart contract.

14

$$D_i\to D_j:{\mathit{Evi}}_{\mathit{CSK}}^{D_i}=SHA-256\left(P,u,b,K,\left(D_i\right),|PubK\left(D_j\right)|,S,E,n,c,K\right)$$

15

$$D_j\to D_i:{\mathit{Evi}}_{\mathit{CSK}}^{D_j}=SHA-256\left({\mathit{SKey}}_{\mathit{OTP}}^{D_i},|{\mathit{Evi}}_{\mathit{CSK}}^{D_i}|,S,E,n,c,K\right)$$

From the above Eqs. (14) and (15), password authentication is ensured by means of evidence of correct session key ‘${\mathit{Evi}}_{\mathit{CSK}}^{D_i}$,’ ${\mathit{Evi}}_{\mathit{CSK}}^{D_j}$ for two distinct devices ‘$D_i$’ and ‘$D_j$,’ respectively. If the device is genuine, the smart contract provides the receiver an access token. The device assembles a package that comprises information such as the 10 subjects 12 different activities obtained via 3 sensor devices. Here, the Ethereum private key is utilized to sign this package and is then sent with the pertinent public key. When the delivery is received, if successful, the device allows the subject’s to access from the IP address of the sender device for the time interval provided. On the other hand, the request is rejected if the test fails. Finally, validation is performed to ensure direct communication to connected or associated devices. The pseudocode representation of Round Hash Index Asymmetric Key Blockchain-based transaction security is given below.

As given in the above algorithm to ensure both data confidentiality and data integrity while performing transactions between blocks while placing it in the chain of network and establishing communication between devices, Ethereum-based blockchain is employed. First, with the optimal unified data storage representation as input, Ethereum-based blockchain is applied as process to model transaction security in smart environment. Initially, key generation is initiated for the devices in the blockchain network. For this Round Hash Index Asymmetric Key is employed. Followed by which, blocks are generated according to specified time interval. Next, authentication verification between devices for placing it in a consecutive block and performing transaction is done via smart contract. Finally, the process of validation is ensured for robust and secure transaction between devices in blockchain network.

Experimental setup

In this section, performance evaluation and validation of the proposed method Radius Divergence-optimized and Round Hash Index Blockchain-based (RD-RHIB) for transaction-secured unified data in smart environment are presented. Detailed comparison with two existing methods, Threat and Risk Assessment for Design of Engineered Systems (TRADES) [1], SoteriaFL [2] and BCAuthEN [21] is analyzed and validated in cloud simulator using JAVA language with the aid of the MHEALTH (Mobile HEALTH) dataset extracted from (https://www.kaggle.com/datasets/gaurav2022/mobile-health). The validation is performed in an Intel Core i5- 6200U CPU @ 2.30 GHz 4 cores with 4 Gigabytes of DDR4 RAM. Simulations are analyzed using seven performance parameters, data confidentiality, data integrity, throughput, packet delivery ratio, service availability, reliability and scalability. To ensure fair comparisons, similar samples are utilized from MHEALTH dataset for all the four methods, RD-RHIB, TRADES [1], SoteriaFL [2] and BCAuthEN [21], respectively, for transaction-secured unified data in smart environment using the same dataset.

The objective of the proposed RD-RHIB focused on improving the data confidentiality and data integrity performance and ensuring security with minimum packet delivery ratio for secured unified data transaction. The three existing methods such as the TRADES [1], SoteriaFL [2] and BCAuthEN [21] are taken as base paper. Hence, these references are selected and compared for proposed RD-RHIB. These three base papers are explained to understand the proposed method. The proposed method concept is derived by considering the problems of these base papers. The drawbacks of these methods are effectively convinced by implementing the proposed method.

Results

In this section, the proposed RD-RHIB method compared with existing TRADES [1], SoteriaFL [2] and BCAuthEN [21] conducted on different performance metrics namely.

• Data confidentiality

• Data integrity

• Throughput

• Packet delivery ratio

• Service availability

• Reliability

• Scalability

• Encryption runtime

The detailed explanation of performance metrics with graphical illustration is described in below section.

Performance analysis of data confidentiality

While designing transaction security for unified data representation method based on blockchain in cloud environment, one of the most significant performance metric is the data confidentiality. The data confidentiality rate is referred to as the percentage ratio of the number of data that are received by the authorized receiver and is mathematically stated as given below:

16

$$\text{DC}=\sum _{i=1}^n\frac{{\text{Samples}}_{\mathit{IR}}}{{\text{Samples}}_i}\ast 100$$

From the above Eq. (16), data confidentiality ‘$\text{DC}$’ is estimated by taking into account the sample data involved in the simulation ‘${\text{Samples}}_i$’ and the sample instances received to the intended recipient ‘${\text{Samples}}_{\mathit{IR}}$.’ It is measured in terms of percentage. Higher value ensures the efficiency of the method. The comparison table of the proposed RD-RHIB method with existing methods Threat and Risk Assessment for Design of Engineered Systems (TRADES) [1], SoteriaFL [2] and BCAuthEN [21] in terms of data confidentiality is shown in Table 1 for data sample ranging between 12 and 120.

Table 1. Tabulation for data confidentiality using RD-RHIB, TRADES [1], SoteriaFL [2] and BCAuthEN [21]

Samples	Data confidentiality
	RD-RHIB	TRADES	SoteriaFL	BCAuthEN
12	91.66	83.33	75	87.5
24	90.35	81.25	73.25	86.45
36	88.25	80.35	71	84.65
48	86.35	78.25	68.35	82.25
60	85	77	65.25	81
72	81.15	75.35	62.35	78.45
84	83.35	76	64	80
96	85	76.85	65.85	81.15
108	90.35	78	70	84.65
120	91	79.35	72.35	86.45

Figure 4 given below shows the data confidentiality rate using the four methods, RD-RHIB, TRADES [1], SoteriaFL [2] and BCAuthEN [21], respectively. From the above table, it is inferred that increasing the number of samples, initially, a small declining inclination is observed using all the four methods. However, with the increase in samples again an increasing trend is observed that corroborates the objective that increasing samples does not compromise the data confidentiality rate. However, simulations performed for the four methods saw better results using RD-RHIB method than [1, 2, 21]. This is evident from the simulation results where 91.66% results were arrived using RD-RHIB method, 83.33% using [1], 75% using [2] and 87.5% using [21] for 10 iterations with an overall sample of 120. The reason for the improvement was due to the application of Round Hash Index Asymmetric Key Generation function. By applying this function, each subject’s device in the blockchain network keeps back a list consisting of the device ID and public key of all other devices. Whenever a subject’s information newly enters the blockchain network, public key is announced by means of message broadcasting. In addition, while transacting the DMS employs the private key for digitally signing the data. Upon reception of data or subject’s information, other subject’s uses the public key of DMS for verification and validation. With this, the sample instances received to the intended recipient were found to be improved using RD-RHIB method, and therefore, substantial improvement observed in data confidentiality also. With an overall of 10 simulation runs, the data confidentiality employing RD-RHIB method was found to be improved by 11% when compared to [1], 27% upon comparison with [2] and 5% upon comparison with [21], respectively.

[See PDF for image]

Fig. 4

Graphical representation of data confidentiality

Performance analysis of data integrity

In this section to validate and analyze the integrity of data, data integrity is evaluated. Data integrity is measured as the percentage ratio of data that are not altered by any users to the overall sample instances. The data integrity is mathematically formulated as given below.

17

$$\text{DI}=\sum _{i=1}^M\frac{{\text{Samples}}_{\mathit{NA}}}{{\text{Samples}}_i}\ast 100$$

From the above Eq. (17), the data integrity ‘$\mathit{DI}$’ is estimated by taking into considerations the sample instances considered for simulation ‘${\text{Samples}}_i$’ and the number of sample data not altered by any malicious users ‘${\text{Samples}}_{\mathit{NA}}$.’ It is measured in terms of percentage and higher rate ensures the efficiency of the method. The results of data integrity arrived at when substituted in Eq. (17) is provided in Table 2.

Table 2. Tabulation for data integrity using RD-RHIB, TRADES [1], SoteriaFL [2] and BCAuthEN [21]

Samples	Data integrity
	RD-RHIB	TRADES	SoteriaFL	BCAuthEN
12	83.33	75	66.66	79.16
24	80.35	73.15	65.25	76.55
36	78.55	72.85	65	75.25
48	75.35	70	64.35	72.45
60	71.35	67.25	64	69.55
72	73.55	70.15	65	72.65
84	78.35	72.35	65.25	75.35
96	81.45	74.55	66	78.45
108	84	76.16	70.25	80.15
120	83.35	74	69.35	78.45

Figure 5 given shows the data integrity using the four methods, RD-RHIB, TRADES [1], SoteriaFL [2] and BCAuthEN [21]. To ensure a fair comparison between the four methods, RD-RHIB, TRADES [1], SoteriaFL [2] and BCAuthEN [21], similar numbers of health data were applied to measure data integrity with an average of 10 simulations runs. From the above table, a decreasing trend was observed increasing the number of samples using all the four methods for an average of 60 samples. However, an increasing trend was observed for greater number of samples. However, comparative figurative representation showed a notable enhancement was found when applying RD-RHIB method than [1, 2, 21]. The reason behind the improvement was owing to the application of Round Hash Index Asymmetric Key Blockchain-based transaction security algorithm that in turn not only preserved the subject’s sample information but also retained the data by managing key in an efficient manner using Round Hash Index Asymmetric Key Generation function. Also by employing time interval-based data writing for generating blocks not a single block made exploitation of the blockchain network where a possible of breaching may said to have been occurred. This in turn aided in minimizing the sample data not to be changed by the malicious users; therefore, improving the data integrity using RD-RHIB method by 9% compared to [1], 19% compared to [2] and 4% compared to [21].

[See PDF for image]

Fig. 5

Graphical representation of data integrity

Performance analysis of throughput, packet delivery ratio, end-to-end delay and encryption run time

Throughput refers to the rate at which information or data packets in the form of blocks are sent through the blockchain network. The packet delivery ratio refers to the percentage ratio of packets in the form of blocks successfully received to the total sent packets in the blockchain network. The throughput rate is mathematically formulated as given below.

18

$$\text{Throughput}=\frac{\text{OUD}}{T}$$

From the above Eq. (18), throughput ‘$\text{Throughput}$’ is estimated on the basis of the optimal unified stored data ‘$\mathit{OUD}$’ that is ready for being sent between the blocks or subjects and the time consumed in processing the optimal unified stored data ‘$T$,’ respectively. It is measured in terms of megabits per second (Mbps). Packet delivery ratio is mathematically represented as given below and is measured in terms of percentage (%).

19

$$\text{PDR}=\frac{{\text{OUD}}_{\text{recv}}}{{\text{OUD}}_{\text{sent}}}\ast 100$$

From the above Eq. (19), the packet delivery ratio ‘$\text{PDR}$’ is estimated taking into consideration the optimal unified data sent ‘${\text{OUD}}_{\text{sent}}$’ and the optimal unified data received ‘${\text{OUD}}_{\text{recv}}$,’ respectively. It is measured in terms of percentage (%). Finally, end-to-end delay of sending a block of device’s data between source and destination over a path consisting of ‘$N$’ links or channels is measured as given below.

20

$${\text{Delay}}_{\text{EE}}=\sum _{i=1}^n{\text{Samples}}_i\ast \left\{\left[t_{\text{act}}\right]-\left[t_{\text{ex}}\right]\right\}$$

From the above Eq. (20), the end-to-end delay ‘${\text{Delay}}_{\text{EE}}$’ is estimated on the basis of the number of samples ‘${\mathit{Samples}}_i$’ involved in the simulation and the difference between the actual time consumed ‘$t_{\text{act}}$’ and the expected time of consumption ‘$t_{\text{ex}}$,’ respectively. It is measured in terms of milliseconds (ms). Encryption runtime is determined as the amount of time taken to encrypt the data to perform secure data transaction. The formula for encryption runtime is given as below.

21

$$E{n}_{\text{Runtime}}={\text{Samples}}_i\ast T\left[\text{ESDT}\right]$$

From (21), encryption runtime ‘${\mathit{En}}_{\text{Runtime}}$’ is calculated. Here, the number of samples is denoted as ‘${\text{Samples}}_i$’ and ‘$T[ESDT]$’ specify a time taken to encrypt single data for secure data transaction. The overall encryption time is measured in terms of milliseconds (ms). Table 3 given below provides the results of throughput, packet delivery ratio, average end-to-end delay and encryption runtime for each of the TRADES [1], SoteriaFL [2], BCAuthEN [21] and the proposed RD-RHIB method for every simulated network.

Table 3. Tabulation for throughput, packet delivery ratio, end-to-end delay and encryption runtime using RD-RHIB, TRADES [1], SoteriaFL [2] and BCAuthEN [21]

Methods	Throughput (Mbps)	Packet delivery ratio (%)	End-to-end delay (ms)	Encryption Runtime (ms)
RD-RHIB	164	95.58	23.25	17
TRADES	158	91.18	43.05	23
SoteriaFL	136	88.03	62	27
BCAuthEN	160	92.46	31.45	20

Figure 6 shows the graphical representation of throughput, packet delivery ratio, End-to-End delay, encryption, Run time using the four methods. From the table representation, it is evident that the throughput rate using proposed RD-RHIB method is found to be comparatively higher than [1, 2, 21]. The reason for the improvement was owing to the application of Jensen–Shannon symmetric divergence where a representation is formulated in such a manner which is hardest to be differentiated from the input matrix by means of a relative entropy. This in turn improves the successful blocks being received. With this the overall throughput using RD-RHIB method by 4% compared to [1], 16% compared to [2] and 3% compared to [21]. Figure 6 also depicts the performance of the four methods in terms of packet delivery ratio. It can be clearly seen that the packet delivery ratio is improved using the proposed RD-RHIB method upon comparison to [1, 2, 21]. This is because hyper graph is used in our work for unified representation theory that with hyper edges association or connection is made between subset of nodes instead of just two nodes. Only upon successful validation results performed by the Ethereum blockchain transaction security is ensured between the subject’s blocks following which packet delivery is made. This in turn results in the enhancement in packet delivery ratio using RD-RHIB method by 5% compared to [1], 4% compared to [2] and 3% compared to [21], respectively. Finally, the figure also portrays the influence of samples to the average end-to-end delay of four different methods. We can see that the average end-to-end delay of proposed RD-RHIB method is comparatively lesser than [1, 2, 21]. This can be explained by the fact that in RD-RHIB method Shannon Information Radius Divergence is applied to evaluate the similarity between two probability distributions (i.e., sensor devices with respect to activities). Owing to the reason that Shannon Information Radius Divergence in proposed method assists in identifying the similarity thus minimizing the end-to-end delay using RD-RHIB method by 46% compared to [1], 31% compared to [2] and 26% compared to [21]. Figure 6 portrays the performance of the four methods in terms of encryption runtime. The average encryption runtime of proposed RD-RHIB method is comparatively lesser than [1, 2, 21]. The reason for lesser encryption runtime is to apply Shannon Information Radius Divergence in proposed RD-RHIB method. By using this method, the similarity among two probability distributions is estimated. Encryption runtime using RD-RHIB method by 26% compared to [1], 37% compared to [2] and 15% compared to [21].

[See PDF for image]

Fig. 6

Graphical representations of throughput, packet delivery ratio, end-to-end delay and encryption runtime

Performance analysis of service availability, reliability and scalability

In this section, detailed analysis of service availability, reliability and scalability is measured. Table 4 given below shows the results obtained using the four methods RD-RHIB, TRADES [1], SoteriaFL [2] and BCAuthEN [21].

Table 4. Tabulation for service availability, reliability and scalability using RD-RHIB, TRADES [1], SoteriaFL [2] and BCAuthEN [21]

Methods	Service availability (%)	Reliability (%)	Scalability (%)
RD-RHIB	96.40	96.80	94.50
TRADES	93.20	93.30	92.40
SoteriaFL	88.40	90.20	83.60
BCAuthEN	94.6	94.8	93

Figure 7 given below shows the graphical representation of service availability, reliability and scalability using the four methods RD-RHIB, TRADES [1], SoteriaFL [2] and BCAuthEN [21]. Service availability refers to the service or subjects information being included as blocks in the blockchain network and accessible to the subjects during the time the DMS promised to keep the service available. In our work by employing time interval-based block generation, the service is said to be available to the subject’s as per request and with this the service availability using RD-RHIB method is said to be improved by 3% compared to [1], 5% compared to [2] and 2% compared to [21]. On the other hand, the reliability is referred to as the probability that a subjects’ information in form of blocks in blockchain network will perform its intended function (i.e., including block) adequately for a specified period of time, or will operate in a defined environment without failure. With time interval-based block generation, the time out is said to occur only when the initialized time interval is greater than time consumption in sending the subject’s device information to the DMS. With this the reliability using RD-RHIB method is said to be improved by 4% compared to [1], 3% compared to [2] and 2% compared to [21]. Finally, scalability refers to the probability of inclusion of any numbers of blocks in blockchain network or the probability to handle the increasing numbers of blocks in the existing network. Also from the above figure, the scalability was improved using RD-RHIB method by 2% upon comparison to [1], 11% upon comparison to [2] and 2% upon comparison to [21], respectively.

[See PDF for image]

Fig. 7

Graphical representations of service availability, reliability and scalability

Discussion

This study compares the proposed RD-RHIB with the existing TRADES [1], SoteriaFL [2] and BCAuthEN [21] using cloud simulator using JAVA language with the MHEALTH dataset based on different parameters namely data confidentiality, data integrity, throughput, packet delivery ratio, service availability, reliability and scalability. In this approach, transaction-secured unified data in smart environment is obtained to enhance the data confidentiality and data integrity. The results confirm that the proposed method is employing different types of performance parameters like 14% and 11% of improved data confidentiality and of data integrity, 8% of enhancing throughput, 4%, 34% and 26% of minimum packet delivery ratio, end-to-end delay and encryption runtime, 3%, 3% and 5% of improved service availability and reliability as well as scalability when compared to existing [1, 2, 21] methods.

Conclusion

With the sizeable increase in both the volume and different data representations, transaction security advances via social media, constituting a devastating menace to the justifiable evolution of society. Secured transaction in unified form assists the organizations based on the business unit to which the user ID or role is associated. Recently, several unified data transaction security algorithms have been designed. In this work, a suitable a Radius Divergence-optimized and Round Hash Index Blockchain-based (RD-RHIB) transaction-secured unified data in smart environment is developed to further improve the data confidentiality and data integrity performance. First, the unified stored data representation obtained from MHEALTH dataset was obtained and subjected to Shannon Information Radius Divergence-based Unified Storage Optimization for optimized data stored therefore ensuring high throughput and packet delivery ratio. Followed by which the optimized unified data storage was applied with a blockchain-based model to first generate asymmetric key via Round Hash Index Asymmetric Key Generation function and then verify authentication using smart contract. Finally, using Round Hash Index Asymmetric Key Blockchain-based transaction security algorithm high data confidentiality and data integrity were said to be ensured. An extensive experimental assessment is done employing different types of performance parameters like data confidentiality, data integrity, throughput, packet delivery ratio, service availability, reliability and scalability with respect to distinct numbers of samples. The overall performance results illustrate that the presented RD-RHIB method achieves higher data confidentiality and data integrity than the traditional methods. Future work should focus on different datasets for enhancing the performance. Novel optimization and blockchain-based secured transaction will be considered for various parameters such as privacy and memory consumption.

Author contributions

Author 1: T. Kalai Selvi She performed the conceptualization, methodology, data collection and writing the study. Author 2: Dr. S. Sasirekha She analyzes the dataset and conceptualization in the study.

Funding

The authors have no funding to report.

Availability of data and materials

Data are publically available within the article. The data have been gathered from https://www.kaggle.com/datasets/gaurav2022/mobile-health.

Declarations

Competing interest

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

Abbreviations

SE

Systems engineering

TRADES

Threat and Risk Assessment for Design of Engineered Systems

UC

Unified communication

AI

Artificial intelligence

CSPs

Cloud service providers

JSD

Jensen–Shannon symmetric divergence

DBMS

Database management system

OUD

Optimal unified data

DMS

Data management servers

TI

Time interval

DC

Data confidentiality

DI

Data integrity

Mbps

Megabits per second

PDR

Packet delivery ratio

MS

Milliseconds

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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