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<p>This paper proposes an intelligent and secure framework for outsourced cloud data storage with privacy preservation, which integrates a Cross Progressive Graph Meerkat contextual convolutional attention network (cross-PGM-2CAN) with a lightweight practical byzantine fault tolerance (PBFT)-based blockchain. The proposed cross-PGM-2CAN incorporates progressive graph convolutional networks with the cross-contextual attention mechanism and optimizes them through the Meerkat optimization algorithm for ranked keyword search over encrypted data with access-pattern privacy preservation. A lightweight blockchain layer employing PBFT consensus provides tamper-proof integrity verification and decentralized auditability. Experimental results show high accuracy with low search latency, low encryption overhead, and significantly improved data confidentiality, thus validating the effectiveness of the framework as a scalable and efficient solution for verifiable, secure, and privacy-aware outsourcing of cloud data.</p>

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An efficient and secure privacy-preserving outsourced cloud data storage using Cross Progressive Graph Meerkat contextual convolutional attention network with secure PBFT-based lightweight blockchain

  • 期刊论文

  • 发表日期：

    2025年12月19日

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  • Abinaya Pandiyarajan,
  • P. Swathika,
  • Senthil Kumar Jagatheesaperumal

摘要

This paper proposes an intelligent and secure framework for outsourced cloud data storage with privacy preservation, which integrates a Cross Progressive Graph Meerkat contextual convolutional attention network (cross-PGM-2CAN) with a lightweight practical byzantine fault tolerance (PBFT)-based blockchain. The proposed cross-PGM-2CAN incorporates progressive graph convolutional networks with the cross-contextual attention mechanism and optimizes them through the Meerkat optimization algorithm for ranked keyword search over encrypted data with access-pattern privacy preservation. A lightweight blockchain layer employing PBFT consensus provides tamper-proof integrity verification and decentralized auditability. Experimental results show high accuracy with low search latency, low encryption overhead, and significantly improved data confidentiality, thus validating the effectiveness of the framework as a scalable and efficient solution for verifiable, secure, and privacy-aware outsourcing of cloud data.