Journal of Intelligent Systems and Internet of Things

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https://doi.org/10.54216/JISIoT

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2690-6791ISSN (Online) 2769-786XISSN (Print)

Volume 16 , Issue 2 , PP: 142-157, 2025 | Cite this article as | XML | Html | PDF | Full Length Article

Deep Learning-based sensitive data detection with optimization-enabled secure encryption model for data privacy preservation in IoT

Mathias Agbeko 1 * , Disha Handa 2

  • 1 University Institute of Computing, Department of Computer Applications, Chandigarh University, Punjab, India - (magbeko@uew.edu.gh)
  • 2 University Institute of Computing, Department of Computer Applications, Chandigarh University, Punjab, India - (disha.e11162@cumail.in)
  • Doi: https://doi.org/10.54216/JISIoT.160211

    Received: December 25, 2024 Revised: February 19, 2025 Accepted: March 09, 2025
    Abstract

    The express expansion of the Internet of Things (IoT) has led to an exponential increase for data being generated and transmitted from various connected devices. This poses significant challenges in terms of data privacy and security, as unauthorized access to such sensitive information can have severe consequences like identity theft or financial fraud. This research proposes a model for sensitive data detection and protection in IoT, based on deep learning and optimization-enabled secure encryption. By combining deep learning-based sensitive data detection and optimization-enabled secure encryption, this model offers a comprehensive solution to preserve data privacy in IoT. The proposed model uses a novel and secure encryption algorithm, ensuring the privacy of the data. An algorithm, Improved Skill Optimization Algorithm (ISOA), which enhances the performance of existing optimization algorithms by incorporating the concept of Double Exponential Smoothing (DES), is proposed for the secure key generation for the data encryption. Data Encryption Standard (DES) is a block cipher algorithm that encrypts and decrypts data using a 56-bit key and 64-bit blocks. The proposed model provides a robust solution for data privacy preservation in IoT networks, which is crucial for protecting sensitive information from unauthorized access and data breaches. The proposed algorithm's performance analysis is evaluated using metrics, like computation time, memory, and fitness function. Results indicate that proposed ISOA based encryption model succeeded a greater performance, with a memory of 0.5170 MB, computational time of 1126.47 sec and fitness value of 1.3630.

    Keywords :

    Deep learning , Internet of Things (IoT) , Privacy preservation , Optimization , Sensitive data , Encryption

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    Cite This Article As :
    Agbeko, Mathias. , Handa, Disha. Deep Learning-based sensitive data detection with optimization-enabled secure encryption model for data privacy preservation in IoT. Journal of Intelligent Systems and Internet of Things, vol. , no. , 2025, pp. 142-157. DOI: https://doi.org/10.54216/JISIoT.160211
    Agbeko, M. Handa, D. (2025). Deep Learning-based sensitive data detection with optimization-enabled secure encryption model for data privacy preservation in IoT. Journal of Intelligent Systems and Internet of Things, (), 142-157. DOI: https://doi.org/10.54216/JISIoT.160211
    Agbeko, Mathias. Handa, Disha. Deep Learning-based sensitive data detection with optimization-enabled secure encryption model for data privacy preservation in IoT. Journal of Intelligent Systems and Internet of Things , no. (2025): 142-157. DOI: https://doi.org/10.54216/JISIoT.160211
    Agbeko, M. , Handa, D. (2025) . Deep Learning-based sensitive data detection with optimization-enabled secure encryption model for data privacy preservation in IoT. Journal of Intelligent Systems and Internet of Things , () , 142-157 . DOI: https://doi.org/10.54216/JISIoT.160211
    Agbeko M. , Handa D. [2025]. Deep Learning-based sensitive data detection with optimization-enabled secure encryption model for data privacy preservation in IoT. Journal of Intelligent Systems and Internet of Things. (): 142-157. DOI: https://doi.org/10.54216/JISIoT.160211
    Agbeko, M. Handa, D. "Deep Learning-based sensitive data detection with optimization-enabled secure encryption model for data privacy preservation in IoT," Journal of Intelligent Systems and Internet of Things, vol. , no. , pp. 142-157, 2025. DOI: https://doi.org/10.54216/JISIoT.160211