# Interaction of Secure Cloud Network and Crowd Computing for Smart City Data Obfuscation

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## Abstract

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## 1. Introduction

- The data collected from the smart cities using the crowd to the cloud computing is proposed by using the AODE classifier.
- The hybrid data obfuscation technique has been used for security purposes for data analysis.
- Also, for retrieving the data, the method of SELECT-APSL is saved in the cloud.

## 2. Literature Review

## 3. Proposed Smart City Data Acquisition

Algorithm 1: Algorithm for Collecting Data from IoT Sensors in Smart Cities. |

Begin Input: A= {a1…an}-instance, R- result // training data For each instance Initially, the sensor finds a neighbour sensor, NN p (b, a) = p (b| a) p (a) $\text{}P(b/a)=\frac{p\left(b\right)p(a|b)}{p\left(a\right)}$ //Compute the probability of b given a: The end for // testing data Repeat For ak ϵ A Compute p (b, a) = p (b, ak) p (a| b, ak) $\text{}P(b/a)=\frac{p\left(b,{a}_{k}\right)p(a|b,{a}_{k})}{p\left(a\right)}$ Get R☐ node NN Return class value Update classifier End for Until Convergence End. |

_{k}belongs to the A, and AODE looks for an estimate of the probability of each class b as follows,

#### 3.1. Crowd Computing in Smart City

#### 3.2. AODE-Based Classification

#### 3.3. Hybrid Data Obfuscation Technique

#### Secure Data Handling by Data Obfuscation

Algorithm 2: Algorithm for Hybrid Data Obfuscation for Security Purposes. |

Input:P_{t}- plaintext, C_T ☐cipher TextOutput:data is ObfuscatedInitially, P_{t}- plaintext with n sizeGet k _{1} For eachk, j = 1,2… <=m findsquare (Sq)N _{t}(k) = P_{t} (k)*k_{1} // value N_{t}(k)Sq(k) = pow (N _{t}(k),2) //Rotate the Sq(k)Get k _{2} //Rotate the RTN at K2 several timesR _{t}(k) = rotate (Sq(k), k_{2}+j) //Find mod for RTN by 256mod(k) = R _{t}(k)%256 //Convert mod into ASCII codeC_T (k) ☐ASCII (mod(k)) C_T☐ cipher text End for |

_{m}). Initially, consider the sensor resulting from numerical data as plaintext with the size of the plain text. Then, the fair value of an Sq(k) and N

_{t}(k) = are computed. The given plaintext is multiplied with the k

_{1}model calculation and stored as N

_{t}. Then calculate a value for k

_{1}, assign a value equal to the m, and multiply plaintext with the k

_{1}.

_{t}(k) = P

_{t}(k)∗k

_{1}

_{2}is used for the qualities of the square. For k

_{2}times, the k

_{2}route also increased, and the fair value is rotated from right to left as Rotate the Sq(k). for upcoming values in Sq(k), k

_{2}+ j, where j, k = 1,2, 3... N.

_{t}(k) = rotate (Sq(k), k

_{2}+ j)

_{2}by adding the mod value. Find R

_{t}(k) mod by 256. R

_{t}(k) mod value is computed by dividing the rotate value by 256. Every mod-value the Creating an ASCII character. The original numerical plaintext and ciphertext are represented by those ASCII letters. To create the ciphertext C T, convert mod(k) into ASCII code. A way to encrypt data without compromising its privacy is via cipher text [2]. The obfuscation creates the ciphertext by blending a variety of ASCII character codes. Each numerical value has a unique cipher text based on the ASCII characteristics code. Each character in the cipher text has an identical plaintext. The plain text and CipherText data sizes may differ [30,31,32].

#### 3.4. Proposed Cryptosystem for Data Management

#### SELECT-APSL

_{d}) and read the input of the Parallel Server as PS

_{in}. This input is selected and retrieved using a SELECT-APSL method [6]. Algorithm 3 shows the data retrieval process using SELECT-ASPL.

Algorithm 3: Algorithm for Data Retrieval Using SELECT-ASPL. |

Initially, the Parallel Server (PS) contains obfuscated hybrid data(O-H_{d}), PP_{d}- pre-processed data, input (Parallel Server)- PS_{in}Input:PS_{in}, PP_{d}Foreach inputSelect ☐input (PS _{in}) // find PS_{in} using APSLIf(specified big data DB = O-H_{d})select ☐ (Join method. (O-H _{d}))O(O-H _{d})☐( O-H_{d})Whilepre-processingpre-processing_data count ≠ select optimum join method return, specified big data DB ≠ O-H_{d}End whileEnd ifIf (PP_{d} ≠ (O-H^{d})) // Compute hit rateIf (hit rate data count ≥ no. of O(O-H_{d}) + no. of PP_{d}) Else // data retrieving rate positively highcheck no. of PP _{d}End if End ifRepeatIf no. of PP_{d}> no. of (PS_{in})execute: Compute the hit rate Else if (no. of PP_{d} < no. of (PS_{in})continue (data retrieving rate positively low) Else rechecks no. of PP_{d}End if (Until termination criterion met)End forReturn accurate data retrieving rate && retrieve data as PSin |

## 4. Result Comparison Discussion with Data Modules

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 8.**The improvement of Data classification improvement percentage for the ASA compared to other methods.

**Figure 11.**The energy consumption after cryptosystem assumption. (

**a**) Energy Consumption In Before Cryptosystem (ASA), (

**b**) Energy Consumption In Before Cryptosystem (MILC-NB), (

**c**) Energy Consumption In Before Cryptosystem (MCCMDC), (

**d**) Energy Consumption In Before Cryptosystem (FAUAV), (

**e**) Energy Consumption In Before Cryptosystem (CAN), (

**f**) Energy Consumption In Before Cryptosystem (B5G).

**Figure 12.**The energy consumption after cryptosystem assumption. (

**a**) Energy Consumption After Cryptosystem (ASA), (

**b**) Energy Consumption After Cryptosystem (MILC-NB), (

**c**) Energy Consumption After Cryptosystem (MCCMDC), (

**d**) Energy Consumption After Cryptosystem (FAUAV), (

**e**) Energy Consumption After Cryptosystem (CAN), (

**f**) Energy Consumption After Cryptosystem (B5G).

No. of Data | Total Packet Delivered (PKT/sec) | |||||
---|---|---|---|---|---|---|

ASA | CAN [13] | MILC-NB [10] | MCCMDC [26] | FAUAV [5] | B5G [4] | |

150 | 18.5 | 17.5 | 16.04 | 15.67 | 10.08 | 7.98 |

200 | 42.54 | 38.79 | 25.45 | 28.65 | 18.40 | 12.45 |

250 | 52.85 | 46.92 | 40.86 | 43.87 | 32.7 | 13.82 |

300 | 62.45 | 57.46 | 45.14 | 59.74 | 32.104 | 14.44 |

350 | 78.87 | 65.36 | 66.91 | 74.70 | 22.84 | 20.71 |

400 | 92.17 | 78.15 | 70.27 | 69.47 | 36.75 | 23.27 |

450 | 98.71 | 82.73 | 75.89 | 80.79 | 40.21 | 28.89 |

500 | 96.27 | 82.27 | 78.35 | 82.94 | 30.42 | 23.35 |

550 | 99.78 | 82.49 | 82.49 | 88.67 | 18.28 | 11.36 |

600 | 118.345 | 101.35 | 118.36 | 106.53 | 22.51 | 13.09 |

No. of Data | Average Energy Consumption (J/sec) | |||||
---|---|---|---|---|---|---|

ASA | CAN | MILC-NB | MCCMDC | FAUAV | B5G | |

150 | 1.023 | 1.024 | 2.005 | 0.001 | 5.782 | 8.246 |

200 | 2.682 | 1.264 | 4.602 | 2.430 | 9.213 | 15.825 |

250 | 3.257 | 1.623 | 3.921 | 1.923 | 11.078 | 18.023 |

300 | 2.213 | 2.891 | 7.218 | 4.132 | 16.057 | 20.021 |

350 | 4.621 | 5.652 | 8.210 | 6.020 | 18.025 | 21.925 |

400 | 6.623 | 7.732 | 9.924 | 5.213 | 20.023 | 23.651 |

450 | 6.623 | 5.592 | 11.253 | 5.582 | 20.023 | 27.365 |

500 | 6.603 | 7.017 | 8.183 | 6.612 | 17.032 | 28.721 |

550 | 6.613 | 4.643 | 8.359 | 4.603 | 20.062 | 31.254 |

600 | 7.126 | 5.621 | 8.129 | 5.621 | 23.457 | 33.521 |

No. of Data | Data Classification Improvement Percentage (%) | ||||
---|---|---|---|---|---|

ASA | MILC-NB | MCCMDC | FAUAV | CAN | |

100 | 24.6 | 23 | 21 | 15 | 9.9 |

150 | 12.87 | 7.5 | 7.5 | 5.5 | 4.8 |

200 | 16 | 12.5 | 12.5 | 8.4 | 5 |

250 | 19 | 17.5 | 14 | 8 | 6 |

300 | 23 | 21 | 19 | 12 | 10 |

350 | 30 | 28.5 | 24 | 13 | 3 |

400 | 36 | 29.6 | 26.2 | 11.3 | 10 |

450 | 38 | 32 | 30 | 10.4 | 9.8 |

No. of Data | Make Span Time (ms) | |||||
---|---|---|---|---|---|---|

B5G | CAN | MILC-NB | MCCMDC | FAUAV | ASA | |

100 | 1165 | 1265 | 1362 | 1403 | 1482 | 1000 |

150 | 1725 | 1853 | 1892 | 1921 | 1989 | 1672 |

200 | 2418 | 2493 | 2513 | 2614 | 2715 | 2381 |

250 | 2951 | 3002 | 3210 | 3351 | 3370 | 2815 |

300 | 3417 | 3582 | 3624 | 3725 | 3816 | 3316 |

350 | 4183 | 4271 | 4521 | 4612 | 4631 | 3852 |

400 | 4723 | 4826 | 5041 | 5262 | 5381 | 4504 |

450 | 5319 | 5412 | 5632 | 5734 | 5982 | 5201 |

No. of Data | Energy Consumption before Cryptosystem | |||||
---|---|---|---|---|---|---|

ASA | MILC-NB | MCCMDC | FAUAV | CAN | B5G | |

100 | 0.29 | 0.31 | 0.34 | 0.36 | 0.37 | 0.38 |

150 | 0.49 | 0.51 | 0.52 | 0.53 | 0.55 | 0.57 |

200 | 0.61 | 0.63 | 0.66 | 0.69 | 0.71 | 0.72 |

250 | 0.78 | 0.8 | 0.82 | 0.84 | 0.86 | 0.92 |

300 | 0.91 | 0.97 | 1.02 | 1.02 | 1.06 | 1.09 |

350 | 0.99 | 1.02 | 1.12 | 1.2 | 1.24 | 1.28 |

400 | 1.08 | 1.17 | 1.2 | 1.34 | 1.4 | 1.45 |

450 | 1.19 | 1.33 | 1.35 | 1.46 | 1.59 | 1.62 |

No. of Data | Energy Consumption after Cryptosystem | |||||
---|---|---|---|---|---|---|

ASA | MILC-NB | MCCMDC | FAUAV | CAN | B5G | |

100 | 0.074 | 0.186 | 0.204 | 0.216 | 0.222 | 0.228 |

150 | 0.094 | 0.306 | 0.312 | 0.318 | 0.33 | 0.342 |

200 | 0.066 | 0.378 | 0.396 | 0.414 | 0.426 | 0.432 |

250 | 0.068 | 0.48 | 0.492 | 0.504 | 0.516 | 0.552 |

300 | 0.046 | 0.582 | 0.612 | 0.612 | 0.636 | 0.654 |

350 | 0.094 | 0.612 | 0.672 | 0.72 | 0.744 | 0.768 |

400 | 0.048 | 0.702 | 0.72 | 0.804 | 0.84 | 0.87 |

450 | 0.024 | 0.798 | 0.81 | 0.876 | 0.954 | 0.972 |

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**MDPI and ACS Style**

Thirumalaisamy, M.; Basheer, S.; Selvarajan, S.; Althubiti, S.A.; Alenezi, F.; Srivastava, G.; Lin, J.C.-W.
Interaction of Secure Cloud Network and Crowd Computing for Smart City Data Obfuscation. *Sensors* **2022**, *22*, 7169.
https://doi.org/10.3390/s22197169

**AMA Style**

Thirumalaisamy M, Basheer S, Selvarajan S, Althubiti SA, Alenezi F, Srivastava G, Lin JC-W.
Interaction of Secure Cloud Network and Crowd Computing for Smart City Data Obfuscation. *Sensors*. 2022; 22(19):7169.
https://doi.org/10.3390/s22197169

**Chicago/Turabian Style**

Thirumalaisamy, Manikandan, Shajahan Basheer, Shitharth Selvarajan, Sara A. Althubiti, Fayadh Alenezi, Gautam Srivastava, and Jerry Chun-Wei Lin.
2022. "Interaction of Secure Cloud Network and Crowd Computing for Smart City Data Obfuscation" *Sensors* 22, no. 19: 7169.
https://doi.org/10.3390/s22197169