# Deep Q-Learning-Based Neural Network with Privacy Preservation Method for Secure Data Transmission in Internet of Things (IoT) Healthcare Application

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## Abstract

**:**

## 1. Introduction

- Constructing a systematic framework using the deep Q-learning method for processing patient data, which further reduces traffic in a network.
- Adoptingciphertext-policy attribute-based privacy preservation (CPABPP) for generating both a public key (PK) and a master key (MK).

## 2. Related Works

## 3. System Model

#### 3.1. Deep Q-Learning-Based Scheduling and Data Transmission

_{1}to f

_{1}, a

_{2}to f

_{2}….a

_{n}to f

_{n}] signifies the system state’s information, and a + b indicates the neuron number in the layer. The output layer representing the information of the selection action is illustrated in Figure 2, which contains the channel (m) in communication mode j and buffer k with a + b + J neurons. The hidden layer is composed of several layers, and the number of neurons is estimated using Equations (1) and (2):

_{i}

_{j}represents the weight between the i-th visible and j-th hidden cells. For the process of encoding and decoding, the logistic sigmoid function is used as a transfer function. The cost function is defined by L(x) [23], as shown in Equation (3):

_{ij}, a

_{i}, b

_{j}}, and the energy function indicates the estimated energy in every node of the visible and hidden layers.

#### System Model for Security

- TA generates the parameters and deals with registration.
- Hospitals supply patients’ medical information to the servers.
- The userqueries the doctors with the source and destination stage.
- Interaction between servers generates a portion of the clinical pathway, which in turn is returned to the user. Multiple interactions are allowed to occur with other interactions instead of between the hospital and user, thereby effectively reducing the local communication overhead and computational cost. In this model, the data (k = 1, 2, 3, …, m) of medical datasets include details of the patients, such as name, age, gender, expenses, other indices, medication, and the time of appointments. These details are shared and protected by the ciphertext-policyattribute-based privacy preservation approach. On the basis of these privacy policies, the server creates an integrated directional connected network.

#### 3.2. Key Generation on Ciphertext-PolicyAttribute-Based Privacy Preservation (CPABPP)

_{i}(KeyGen(MK,S)),where ss

_{i}(s) is a function that produces the share as (n, k) secret sharing is applied on secrets. Once the setup algorithm is executed, data users and the i-th authority receive PK

_{F}and SK [f

_{i}] sent by the data owner over the secure channel, respectively. In this process, a large number of subkeys are used, which are precomputed before the process of decryption or encryption. The P-array contains 18 to 32 bit subkeys: P1, P2, ..., P18.

Algorithm 1 Algorithm for generating subkeys |

1: Input—plain text |

2: Output—subkeys |

3: Strings(x) = P1, P2, P3…Pn |

4: if |

5: A = P1(XOR) P2 |

6: (n = P1; n + 1 > P1) |

7: B = P2(XOR) P3 |

8: (P2 = n; P2 < n + 1) |

9: C = P3(XOR) P4 |

10: (n = P1; n + 1 > P1) |

11: N = Pn(XOR) Pn |

12: (P1 = n; P1 < n + 1) |

13: end if |

14: Y*Z* (A mod E) = K1 |

15: X*Z*(B mod F) = K2 |

16: Y*X*(C mod G) = K3 |

17: α^{2} (Y*Z*(A mod E)) = βK1 |

18: α^{2} (X*Z*(B mod F)) = βK2 |

19: α^{2} (Y*X*(C mod G)) = βK3 |

20: end |

#### Encryption Process

_{b}, which is the initial S-box. The tent–logistic mapis then repeated Ltimes togenerate a chaotic series with lengthL. The sensitivity of the chaotic series is improved, as the first (L-256) elements are discarded from the actual chaotic series, therebyobtaining a new chaotic series with length 256 denoted as X. By sorting X, J = {J(1), J(2), ..., J(256)}, an index array, is obtained. As the chaotic series is nonperiodic and ergodic, it certainly gives J(i) ≠ J(j), provided that I ≠ j.

_{0}represent adjacent data records, and O indicates the set of data received as output. R ∈ S achieves data privacy, but for several hospitals, Laplace is put into local training model ${\mathrm{m}}_{\mathrm{i}}$, as shown in Equation (12):

_{i}indicate the communication and transaction cost, respectively. Sharing personal data is a risk for data providers due to some specific security attacks. This can be overcomeby simply transmitting data to the user with valid details to the requester, and preserving the data privacy of the holders. Rather than sharing actual data, learned models alone can be exchanged by the provided data, such as hospitals with the requester.

## 4. Performance Analysis

_{pack}) transmitted from node x to node y in less time. The formula for communication overhead is given in Equation (17):

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

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**MDPI and ACS Style**

Kathamuthu, N.D.; Chinnamuthu, A.; Iruthayanathan, N.; Ramachandran, M.; Gandomi, A.H.
Deep Q-Learning-Based Neural Network with Privacy Preservation Method for Secure Data Transmission in Internet of Things (IoT) Healthcare Application. *Electronics* **2022**, *11*, 157.
https://doi.org/10.3390/electronics11010157

**AMA Style**

Kathamuthu ND, Chinnamuthu A, Iruthayanathan N, Ramachandran M, Gandomi AH.
Deep Q-Learning-Based Neural Network with Privacy Preservation Method for Secure Data Transmission in Internet of Things (IoT) Healthcare Application. *Electronics*. 2022; 11(1):157.
https://doi.org/10.3390/electronics11010157

**Chicago/Turabian Style**

Kathamuthu, Nirmala Devi, Annadurai Chinnamuthu, Nelson Iruthayanathan, Manikandan Ramachandran, and Amir H. Gandomi.
2022. "Deep Q-Learning-Based Neural Network with Privacy Preservation Method for Secure Data Transmission in Internet of Things (IoT) Healthcare Application" *Electronics* 11, no. 1: 157.
https://doi.org/10.3390/electronics11010157