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<p>With the exponential growth in the number of Internet of Things (IoT) devices, user privacy concerns have become increasingly pressing. Real-time customization and management of privacy preferences are made possible by the incorporation of adaptive privacy controls. Users are empowered by these controls to decide for themselves how their personal data is used and shared in IoT situations. As a result, the problem of data-sharing privacy becomes more pressing, particularly when malicious requests from IoT devices try to use edge nodes to retrieve private data from the cloud storage system. Therefore, this work suggests a privacy preservation framework using an approach based on Impact factor-driven improved association rule mining to increase data preservation and protection against malicious attacks. The two key components of the model are (1) data restoration and (2) data sanitization. The two stages of the data sanitization method are key generation and impact factor-based improved association rule mining. The association rules are first produced using an enhanced Apriori algorithm in the Impact factor-based improved association rule mining phase. To produce the ideal key in this situation, a brand-new hybrid optimization technique centred on the Jellyfish Assisted Pelican Optimization Algorithm (JAPOA) is presented. The produced association rules and the best keys are then subjected to a bitwise Exclusive OR (XOR) operation to achieve data sanitization. Sensitive information is protected due to this procedure. Additionally, the original sensitive material is successfully recovered during the data restoration phase by applying the reverse bitwise XOR operation to the sanitized data. The JAPOA approach achieves a superior privacy preservation ratio of 2.735 for data variation 1, while traditional schemes obtained lower privacy preservation ratings. By utilizing the hybrid optimization technique and XOR-based sanitization, the proposed model ensures robust security for IoT data while allowing for accurate restoration.</p>

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Impact factor-based improved association rule mining-based privacy preservation model for IoT with hybrid optimal key generation

  • 期刊论文

  • 发表日期：

    2026年2月12日

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  • Ravindra Sadashivrao Apare,
  • Manoj Limchand Bangare,
  • Nitin Sudam More,
  • Pushpa Manoj Bangare,
  • Ratnaprabha Ravindra Borhade

摘要

With the exponential growth in the number of Internet of Things (IoT) devices, user privacy concerns have become increasingly pressing. Real-time customization and management of privacy preferences are made possible by the incorporation of adaptive privacy controls. Users are empowered by these controls to decide for themselves how their personal data is used and shared in IoT situations. As a result, the problem of data-sharing privacy becomes more pressing, particularly when malicious requests from IoT devices try to use edge nodes to retrieve private data from the cloud storage system. Therefore, this work suggests a privacy preservation framework using an approach based on Impact factor-driven improved association rule mining to increase data preservation and protection against malicious attacks. The two key components of the model are (1) data restoration and (2) data sanitization. The two stages of the data sanitization method are key generation and impact factor-based improved association rule mining. The association rules are first produced using an enhanced Apriori algorithm in the Impact factor-based improved association rule mining phase. To produce the ideal key in this situation, a brand-new hybrid optimization technique centred on the Jellyfish Assisted Pelican Optimization Algorithm (JAPOA) is presented. The produced association rules and the best keys are then subjected to a bitwise Exclusive OR (XOR) operation to achieve data sanitization. Sensitive information is protected due to this procedure. Additionally, the original sensitive material is successfully recovered during the data restoration phase by applying the reverse bitwise XOR operation to the sanitized data. The JAPOA approach achieves a superior privacy preservation ratio of 2.735 for data variation 1, while traditional schemes obtained lower privacy preservation ratings. By utilizing the hybrid optimization technique and XOR-based sanitization, the proposed model ensures robust security for IoT data while allowing for accurate restoration.