ABSTRACT The rapid adoption of cloud computing (CC) has intensified the demand for robust key management services (KMS) to safeguard data amid escalating privacy and security concerns. Despite KMS being pivotal to securing this increasingly vital technology for modern enterprises, existing literature often lacks a comprehensive, user‐centric synthesis of KMS techniques and their practical implications. This systematic literature review (SLR) bridges this gap by analysing 53 significant studies from 2011 to 2024, evaluating KMS in CC with a focus on security, privacy, and operational challenges. Our key contributions include an innovative taxonomy classifying KMS techniques, thoroughly analysing cryptographic methods, and identifying critical research gaps, such as latency reduction and enhanced data privacy. Employing taxonomic classification, meta‐analysis, and architectural evaluation, we assessed KMS solutions, including tools like AWS KMS and CloudHSM, revealing substantial progress in KMS architectures and privacy‐preserving cryptography. However, persistent challenges, such as latency, scalability, and regulatory compliance, underscore the need for innovative cryptographic and architectural advancements. This survey delivers a comprehensive knowledge base and taxonomy to steer future KMS research in CC, advancing the state of the art by equipping researchers and practitioners with a structured foundation to design more secure, efficient, and compliant cloud systems.

Cloud Computing (CC) is widely adopted in sectors like education, healthcare, and banking due to its scalability and cost-effectiveness. However, its internet-based nature exposes it to cyber threats, necessitating advanced security frameworks. Traditional models suffer from high false positives and limited adaptability. To address these challenges, VECGLSTM, an attack detection model integrating Variable Long Short-Term Memory (VLSTM), capsule networks, and the Enhanced Gannet Optimization Algorithm (EGOA), is introduced. This hybrid approach enhances accuracy, reduces false positives, and dynamically adapts to evolving threats. EGOA is employed for its superior optimization capability, ensuring faster convergence and resilience. Additionally, Chaotic Cryptographic Pelican Tunicate Swarm Optimization (CCPTSO) is proposed for privacy-preserving key management. This model combines chaotic cryptographic techniques with the Pelican Tunicate Swarm Optimization Algorithm (PTSOA), leveraging the pelican algorithm’s exploration strength and the tunicate swarm’s exploitation ability for optimal encryption security. Performance evaluation demonstrates 99.675% accuracy, 99.5175% recall, 99.7075% precision, and 99.615% F1-score, along with reduced training (1.79s), encryption (0.986s), and decryption (1.029s) times. This research significantly enhances CC security by providing a scalable, adaptive framework that effectively counters evolving cyber threats while ensuring efficient key management.

Summary The exponential growth of data poses a critical challenge for cloud storage systems. Redundant data consumes valuable storage space and increases infrastructure costs. Data deduplication, a technique for eliminating duplicate data copies, offers a promising solution. However, existing deduplication techniques often compromise data security, especially when dealing with encrypted data. This paper proposes a novel approach that merges convergent encryption (CE) with data deduplication. CE leverages user data itself to generate unique encryption keys, enabling secure deduplication on encrypted data. We analyze existing literature on secure data deduplication and categorize various techniques using UML activity diagrams. We then present our proposed CE‐based deduplication system, outlining its functionalities through UML diagrams. This research contributes to the field of secure data storage by proposing a novel and secure deduplication approach. By demonstrating its efficiency and security benefits, this work paves the way for more efficient and secure cloud storage solutions. Finally, we demonstrate the system's effectiveness through a comparative analysis, highlighting its potential to significantly improve storage efficiency while maintaining data security.

This letter deals with a novel and versatile bridge setup for the direct measurement of in-circuit resistance. Unlike other directreading bridges, it is a balanced, active bridge. It basically employs an active RC integrator, a fixed standard resistance, and a reference voltage source. The steady-state output voltage of the integrator is found to be directly proportional to the unknown resistance. Test results show the accuracy of measurement to be of the order of 1 percent in the range of 0.4 to 60 kΩ.