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<p>Cloud computing offers a flexible and cost-effective solution for accessing online services from anywhere at any time. However, data security remains a major concern, particularly because Cloud Service Providers (CSPs) have access to users’ sensitive information. Existing cloud storage approaches also face limitations, including reliance on standard encryption, suboptimal data distribution, limited scalability across multiple clouds, and vulnerability to attacks, challenges that are especially critical in privacy-sensitive domains such as healthcare and finance. To address this privacy issue, a secure and efficient system, Brown Bear Red Panda Optimization (BBRPO) is proposed for data distribution in the cloud. The model consists of two main phases: data storage and data retrieval. In the storage phase, input data are divided into several chunks using Coupling-based Clustering, with inter-cluster distances measured by the proposed BBRPO, which is derived by integrating Brown Bear Optimization (BBO) and Red Panda Optimization (RPO). These chunks are then grouped and stored as blocks using a Cohesion-based Clustering algorithm. Each block is encrypted using the Advanced Encryption Standard (AES) and stored across its corresponding clouds. In the retrieval phase, the retrieval manager searches the cloud for the requested data. After locating it, the data is decrypted from the stored blocks and their respective chunks. This approach enhances data security and improves trust in cloud computing. The efficacy of BBRPO is analyzed based on metrics that include encryption time, decryption time, memory, intercluster distance, intracluster distance, latency, and throughput, with values of 0.425 sec, 0.281 sec, 5.801 MB, 4.622 m, 7.289 m, 0.254 ms, and 8.673 Mbps, respectively. Additionally, the clustering performance shows a Silhouette Score of 0.896 and a Davies–Bouldin Index of 0.08 with 6 clusters, indicating improved cluster cohesion and separation.</p>

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Coupling and cohesion-based clustering approach in multi-cloud storage for confidential data using brown bear-red panda optimization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月2日

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  • Anand R,
  • S. Varadhaganapathy

摘要

Cloud computing offers a flexible and cost-effective solution for accessing online services from anywhere at any time. However, data security remains a major concern, particularly because Cloud Service Providers (CSPs) have access to users’ sensitive information. Existing cloud storage approaches also face limitations, including reliance on standard encryption, suboptimal data distribution, limited scalability across multiple clouds, and vulnerability to attacks, challenges that are especially critical in privacy-sensitive domains such as healthcare and finance. To address this privacy issue, a secure and efficient system, Brown Bear Red Panda Optimization (BBRPO) is proposed for data distribution in the cloud. The model consists of two main phases: data storage and data retrieval. In the storage phase, input data are divided into several chunks using Coupling-based Clustering, with inter-cluster distances measured by the proposed BBRPO, which is derived by integrating Brown Bear Optimization (BBO) and Red Panda Optimization (RPO). These chunks are then grouped and stored as blocks using a Cohesion-based Clustering algorithm. Each block is encrypted using the Advanced Encryption Standard (AES) and stored across its corresponding clouds. In the retrieval phase, the retrieval manager searches the cloud for the requested data. After locating it, the data is decrypted from the stored blocks and their respective chunks. This approach enhances data security and improves trust in cloud computing. The efficacy of BBRPO is analyzed based on metrics that include encryption time, decryption time, memory, intercluster distance, intracluster distance, latency, and throughput, with values of 0.425 sec, 0.281 sec, 5.801 MB, 4.622 m, 7.289 m, 0.254 ms, and 8.673 Mbps, respectively. Additionally, the clustering performance shows a Silhouette Score of 0.896 and a Davies–Bouldin Index of 0.08 with 6 clusters, indicating improved cluster cohesion and separation.