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<p>Fog computing is an emerging technology that many sectors and industries are currently investing in to establish their own real-time and low-latency environments. Unlike the traditional Internet of Things (IoT), the fog-based IoT systems offers improved performance efficiency and enhanced data security by providing local mechanisms for pre-processing, filtering, and forwarding of data. However, the incorporation of fog technologies into the IoT has specific challenges with security and privacy. This is because fog nodes are located at the edge of the network, that may lack full reliability and trustworthiness. This work presents a novel scheme called PHE-SDA for aggregating a subset of response data while preserving privacy. The proposed PHE-SDA scheme is specifically designed for scenarios involving Fog-Cloud based IoT systems. It enables end-users to calculate the total amount of data collected from a certain group of smart devices. The subset is determined through securely calculating the inner product to measure the similarity between the normalized feature vectors of the end user's request and the response from each IoT device. If the inner product surpasses the threshold specified by the user, the data from the IoT device will be securely incorporated into the aggregation process to produce the ultimate outcome. In order to successfully carry out privacy-enhancing subset data aggregation in the Fog-Cloud based IoT system, we employ the Paillier homomorphic cryptography technique to encrypt the user's desired feature vector, similarity threshold, data sensed by IoT device, and some intermediate results. This work is an innovative initiative to effectively address the problem of prioritizing privacy while aggregating subsets in fog-based IoT systems. Subsequently, this work presents a comprehensive analysis and assessment of the effectiveness of the proposed PHE-SDA scheme. The comprehensive evaluation and results indicate that our proposed PHE-SDA scheme successfully performs privacy-preserving subset aggregation, leading to significant reductions in computational time and communication costs.</p>

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PES-DAS: Privacy Enhancing Subset-Data Aggregation Scheme Leveraging Homomorphic Cryptosystem in Fog-Cloud Based IoT Applications

  • 期刊论文

  • 发表日期：

    2025年3月14日

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  • Prashant Shukla,
  • Sudhakar Pandey

摘要

Fog computing is an emerging technology that many sectors and industries are currently investing in to establish their own real-time and low-latency environments. Unlike the traditional Internet of Things (IoT), the fog-based IoT systems offers improved performance efficiency and enhanced data security by providing local mechanisms for pre-processing, filtering, and forwarding of data. However, the incorporation of fog technologies into the IoT has specific challenges with security and privacy. This is because fog nodes are located at the edge of the network, that may lack full reliability and trustworthiness. This work presents a novel scheme called PHE-SDA for aggregating a subset of response data while preserving privacy. The proposed PHE-SDA scheme is specifically designed for scenarios involving Fog-Cloud based IoT systems. It enables end-users to calculate the total amount of data collected from a certain group of smart devices. The subset is determined through securely calculating the inner product to measure the similarity between the normalized feature vectors of the end user's request and the response from each IoT device. If the inner product surpasses the threshold specified by the user, the data from the IoT device will be securely incorporated into the aggregation process to produce the ultimate outcome. In order to successfully carry out privacy-enhancing subset data aggregation in the Fog-Cloud based IoT system, we employ the Paillier homomorphic cryptography technique to encrypt the user's desired feature vector, similarity threshold, data sensed by IoT device, and some intermediate results. This work is an innovative initiative to effectively address the problem of prioritizing privacy while aggregating subsets in fog-based IoT systems. Subsequently, this work presents a comprehensive analysis and assessment of the effectiveness of the proposed PHE-SDA scheme. The comprehensive evaluation and results indicate that our proposed PHE-SDA scheme successfully performs privacy-preserving subset aggregation, leading to significant reductions in computational time and communication costs.