UNLOCKING SECURE SEARCH: KEY-AGGREGATE TECHNIQUES FOR ENCRYPTED CLOUD DATA RETRIEVAL

Abstract

Cloud computing has revolutionized data storage and sharing by providing scalable and cost-effective solutions for individuals and organizations. However, outsourcing sensitive data to cloud servers raises significant security and privacy concerns, particularly regarding unauthorized access and data leakage. Encryption is widely adopted to protect data confidentiality, but it introduces challenges in performing efficient data retrieval operations such as keyword search over encrypted data. Traditional searchable encryption techniques allow users to search encrypted data, but they often suffer from limitations such as high computational overhead, lack of scalability, and inefficient key management. To address these issues, this research proposes a secure key-aggregate keyword retrieval scheme that enables efficient and secure search operations over encrypted cloud data. The proposed framework integrates key-aggregate cryptography with searchable encryption to allow users to generate a single compact aggregate key for accessing multiple encrypted data files. This reduces key management complexity and enhances system scalability. Additionally, the scheme incorporates keyword indexing and secure trapdoor generation mechanisms to enable efficient search operations without revealing sensitive information to the cloud server. The system ensures data confidentiality, query privacy, and resistance against various attacks such as keyword guessing and collusion attacks. Furthermore, the proposed model optimizes search efficiency by reducing computation and communication overhead, making it suitable for large-scale cloud environments. Experimental results demonstrate improved performance in terms of search time, storage efficiency, and security compared to existing methods. The scheme also supports dynamic data updates and flexible access control, enhancing usability in real-world applications such as healthcare, finance, and enterprise data management. Overall, this research presents a robust and scalable solution for secure data retrieval in cloud computing environments, addressing key challenges in privacy-preserving data access.