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BBNSF: Blockchain-Based Novel Secure Framework Using RP^{2}-RSA and ASR-ANN Technique for IoT Enabled Healthcare Systems

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## Abstract

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^{2}-RSA) algorithm for providing security. Secondly, feature selection is completed by employing the correlation factor-induced salp swarm optimization algorithm (CF-SSOA). Finally, health status classification is performed using advanced weight initialization adapted SignReLU activation function-based artificial neural network (ASR-ANN) which classifies the status as normal and abnormal. Meanwhile, the abnormal measures are stored in the corresponding patient blockchain. Here, blockchain technology is used to store medical data securely for further analysis. The proposed model has achieved an accuracy of 95.893% and is validated by comparing it with other baseline techniques. On the security front, the proposed RP

^{2}-RSA attains a 96.123% security level.

## 1. Introduction

^{2}RSA for encrypting and decrypting the data. The feature selection is performed using CF-SOA. Additionally, the health status classification is completed using advanced weight initialization adapted SignReLU activation function-based artificial neural network (ASR-ANN) which classifies the status as normal and abnormal. The remainder of the paper is outlined as follows: Section 2 describes some of the existing methodologies related to the proposed work. A detailed description of the proposed framework is mentioned in Section 3. In Section 4, the result of the proposed model and its comparisons are presented. At last, in Section 5, the paper is concluded along with future work.

## 2. Background and Related Work

## 3. Proposed Methodology

^{2}RSA is made subsequent to authentication. Subsequently, the HS prediction system accepts the sensed data as the input. It envisages the normal as well as abnormal health conditions of patients and also transfers the alert message. For additional analysis, the abnormal data are amassed in the BC that aids the doctor to follow the record. For separating the normal and abnormal data, data collection, feature extraction (FE), and feature selection (FS), together with the classification process are carried out in the HS prediction system. The classification is completed utilizing the ASR-ANN technique. If the data are found to be abnormal an alert is sent to the patient. The dataset utilized consisted of 76 features and CF-SSOA was utilized for feature selection. The proposed technique’s structure is displayed in Figure 1.

#### 3.1. Registration Phase

- ❖
- Let β be the patient unique ID and the timestamp of the patient at the registration time is represented as T. $\alpha $ is the string of bytes for which checksum is to be calculated. The patient ID and timestamp are combined together to convert them into hash codes. It is shown below.$$\alpha =\beta \mathrm{{\rm T}}$$
- ❖
- Next, the hash code is generated for the combined values using the Adler-32 hashing technique which is mathematically expressed below. Here, 65521 represents the largest prime number smaller than 2
^{16}.$$G=1+{\alpha}_{1}+{\alpha}_{2}+\dots \dots +{\alpha}_{n}\cdot \left|65521\right|$$$$H=\u2329\left(1+{\alpha}_{1}\right)+\left(1+{\alpha}_{1}+{\alpha}_{2}\right)+\dots +\left(1+{\alpha}_{2}+\dots +{\alpha}_{n}\right)\u232a\cdot \left(\right)open="|">65521\left(\right|$$ - ❖
- The hash code can be formed as follows,$$Adl\left(\alpha \right)=\left(H\times 65521\right)+G$$

#### 3.2. Blockchain

#### 3.3. Login

#### 3.4. User Authentication

#### 3.5. Data Encryption Using RP^{2}-RSA

^{2}-RSA. The mathematical model of the RP

^{2}-RSA is illustrated as,

#### 3.5.1. Key Generation

- ❖
- At first, ‘2′ large prime numbers $A,B$ are randomly chosen and gauge $P=A\ast B$
- ❖
- Next, the Euler totient function $\phi \left(P\right)$ is gauged as,$$\phi \left(P\right)=\left(A-1\right)\ast \left(B-1\right)$$
- ❖
- Choose a random integer $r$ centered on the subsequent condition, $\mathrm{gcd}\left(r.\phi \left(P\right)=1\right)$ and $1<r<\phi \left(n\right)$ and calculate,$$w={r}^{-1}\cdot \mathrm{mod}\u2329\phi \left(P\right)\u232a$$$$r\ast w=1\cdot \mathrm{mod}\u2329\phi \left(P\right)\u232a$$
- ❖
- Next, these public keys are joined with the private keys and reversed to make the secret key for ameliorated security. The secret key $\left(\delta \right)$ is generated utilizing the subsequent equation,$$\delta =rw=>wr+Adl\left(\alpha \right)$$

#### 3.5.2. Encryption

#### 3.5.3. Decryption

#### 3.6. Health Status Prediction

#### 3.6.1. Pre-Processing

#### 3.6.2. Feature Extraction

#### 3.6.3. Feature Selection Using CF-SSOA

**Step 1:**Let the initial swarm population be stated as $\left\{{s}_{i}={s}_{1},{s}_{2},\dots \dots {s}_{n}\right\}$ (extracted features) in a $d$-dimensional search space. The salp swarms’ target is signified by the food source $\left(\mathrm{\Gamma}\right)$. Initially, centered on the fitness calculation, the leader salp is chosen.

**Step 2:**Every Salp’s fitness $f\left({s}_{i}\right)$ in the Salp population ${s}_{i}$ is computed via the closest distance betwixt the salp and food source. The fittest salp is chosen as the group’s leader. The followers are the remaining ones in the group. In searching for the target (food source), the leader salp guides the whole populace.

**Step 3:**Next, the position of the leader as well as the followers closest to the target is updated utilizing the equation below,

**Step 4:**Centered on the subsequent formula, the correlation factor is estimated

**Step 5:**The adjustment parameter ${\xi}^{1}$ is used for balancing the salps’ exploration as well as exploitation, which is gauged as,

**Step 6:**Next, for updating the followers’ position, Newton’s law of motion is employed, which is illustrated mathematically below,

Algorithm 1: Pseudocode for the proposed CF-SSOA |

Input: Extracted features ${s}_{i}$Output: Selected feature ${x}_{j}$BeginGenerate initial swarm populationInitialize the food source $\mathrm{\Gamma}$For $i=1:n$Compute the fitness $f\left({s}_{i}\right)$ of each ${s}_{i}$Choose the leader ${s}_{i}^{L}$ and followers ${s}_{i}^{F}$While $\left(i<Y\right)$Update ${\xi}^{1}=2\cdot \mathrm{exp}{\left(-\frac{4y}{Y}\right)}^{2}$Calculate the correlation factor ${\tau}^{1},{\tau}^{2}$If $i==1$Renew the position of leaderElseKeep posted the follower’s positionEnd ifImprove the salps based on upper and lower bounds End whileEnd forReturn ${x}_{j}$End |

#### 3.6.4. Classification via ANN

**Step 1:**To commence the classification process, the chosen features ${x}_{j}$ are inputted to the SR-ANN’s IL and move towards the HL. In the HL, each input feature is multiplied with the input-hidden weight value $\left({w}_{IH}\right)$. The hidden layer’s output $\left({h}_{l}\right)$ is written as.

**Step 2:**The SignReLU AF that is utilized in the HL is rendered by,

**Step 3:**Next, the last output in the output layer $\left(O\right)$ of the SR-ANN is computed utilizing the equation below,

**Step 4:**Next, the cross-entropy loss function method evaluated the error value,

## 4. Result and Discussion

#### 4.1. Performance Evaluation of RP^{2}RSA

^{2}RSA methodology, the data on the WBS is encrypted to transmit the data safely. The time consumed to encrypt the data by the RP

^{2}RSA method is exhibited in Figure 4. Compared with the number of data Vs ET, the representation of the graph is made, which states that the augmentation in the data size increases the ET. Thus, in the RP

^{2}RSA methodology, the ET is 14,986 ms if the total data are less (200). The ET is 17,042 ms if there is a maximum of data (1000). However, the ET of the prevailing RSA is 19,124 ms for 200 numbers of data and this value is high when analogized to the RP

^{2}RSA technique. Similarly, for the other prevailing methodologies, there is a variation in the ET. The RP

^{2}RSA technique encrypts the data with a lesser amount of time than the other techniques which are evident in the graph. From Figure 5. a comparison of decryption time can well be seen clearly. The time consumed to decrypt the data after encryption is measured by the DT. A good performance is implied by less decryption time, the same as ET. By altering the number of data, the DT of the RP

^{2}RSA is given as 14,121 ms (200), 14,998 ms (400), 15,763 ms (600), 16,034 ms (800), and 16,874 ms (1000), in Figure 6. In contrast, 24,761 ms (200), 27,532 ms (400), 28,873 ms (600), 30,302 ms (800), and 32,442 ms (1000) are the DT of the prevailing ECC. The DT of the RP

^{2}RSA technique is lesser than the DT of the prevailing ECC. Likewise, there is a deviation in the prevailing RSA and Elagamal’s decryption time. Thus, in comparison with the other prevailing methods, the RP

^{2}RSA methodology is superior.

^{2}RSA displays less time (2124 ms), and is revealed in Figure 6. On the contrary, to generate keys, the prevailing RSA needs 2662 ms, and the existent ECC and Elagamal take 3154 ms and 3548 ms, respectively. However, the only minimum time is taken by the RP

^{2}RSA technique. Therefore, by correlating with the prevailing method, the RP

^{2}RSA’s performance is better. From Figure 7. a comparison of key generation time can well be seen clearly.

^{2}RSA is evaluated. The SL acquired by the BC- RP

^{2}RSA is correlated with the prevailing MedSBA (medical data sharing centered on blockchain technology), RSA, and ECC in Figure 7. The BC- RP2RSA method accomplishes 96.123% of greater security which is proven by the evaluation. However, relatively, the prevailing system has a lower SL. Therefore, BC technology together with data encryption techniques is employed for data security and data encryption techniques. Thus, regarding the SL, the proposed structure has achieved superior performance.

#### 4.2. Superiority Measure of the Proposed Classifier

## 5. Conclusions

^{2}RSA is proposed for securely transferring the patient IoT data in the CS. By contrasting the outcomes with some baseline techniques, the performances are estimated. The 96.123% SL with fewer times 48,432 ms is attained by the proposed work. The proposed model’s classification accuracy, and precision, together with the recall are 95.893%, 95.654%, as well as 96.098% correspondingly. Additionally, it needs less memory of 165,432 kb. It is apparent from the overall analysis as well as discussions, analogized to the top-notch methodologies that the proposed work performs better with higher security and accuracy and executes in a lesser time. In the future, the work will be ameliorated regarding the data load along with network failures utilizing advanced deep learning techniques. Furthermore, we would intend to utilize the proposed framework in other application scenarios of IoT.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

RP^{2}-RSA. | Reversed public-private keys combined Rivest–Shamir–Adleman |

CF-SSOA | Correlation factor induced salp swarm optimization algorithm |

ASR-ANN | Adapted SignReLU activation function centered artificial neural network |

IoMT | Internet of Medical Things |

BC | Blockchain |

HS | Health Status |

PID | Patient ID |

DID | Device ID |

ANN | Artificial neural network |

CNN | Convolutional neural network |

RNN | Recurrent neural network |

DBNN | Deep belief neural network |

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Techniques | Sensitivity (%) | Specificity (%) | Recall (%) |
---|---|---|---|

Proposed ASR-ANN | 96.146 | 95.959 | 96.098 |

ANN | 95.531 | 95.089 | 95.595 |

CNN | 94.982 | 94.375 | 94.121 |

RNN | 94.019 | 93.602 | 93.843 |

DBNN | 93.158 | 92.428 | 92.204 |

Techniques/Performance Metric | Memory Usage (kb) |
---|---|

Proposed ASR-ANN | 165,432 |

ANN | 198,791 |

CNN | 213,490 |

RNN | 246,701 |

DBNN | 278,932 |

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## Share and Cite

**MDPI and ACS Style**

Kumar, M.; Mukherjee, P.; Verma, S.; Kavita; Kaur, M.; Singh, S.; Kobielnik, M.; Woźniak, M.; Shafi, J.; Ijaz, M.F.
BBNSF: Blockchain-Based Novel Secure Framework Using RP^{2}-RSA and ASR-ANN Technique for IoT Enabled Healthcare Systems. *Sensors* **2022**, *22*, 9448.
https://doi.org/10.3390/s22239448

**AMA Style**

Kumar M, Mukherjee P, Verma S, Kavita, Kaur M, Singh S, Kobielnik M, Woźniak M, Shafi J, Ijaz MF.
BBNSF: Blockchain-Based Novel Secure Framework Using RP^{2}-RSA and ASR-ANN Technique for IoT Enabled Healthcare Systems. *Sensors*. 2022; 22(23):9448.
https://doi.org/10.3390/s22239448

**Chicago/Turabian Style**

Kumar, Mohit, Priya Mukherjee, Sahil Verma, Kavita, Maninder Kaur, S. Singh, Martyna Kobielnik, Marcin Woźniak, Jana Shafi, and Muhammad Fazal Ijaz.
2022. "BBNSF: Blockchain-Based Novel Secure Framework Using RP^{2}-RSA and ASR-ANN Technique for IoT Enabled Healthcare Systems" *Sensors* 22, no. 23: 9448.
https://doi.org/10.3390/s22239448