A Comprehensive Review of Optimization-Driven Design of Attribute-Based Encryption Schemes: Security Models, Optimization Techniques, and Emerging Computing Applications

Abstract

Attribute-Based Encryption (ABE) has emerged as a powerful cryptographic primitive for enabling fine-grained access control in distributed and cloud computing environments. However, traditional ABE schemes suffer from high computational overhead, inefficient decryption processes, and scalability limitations. To address these challenges, optimization-driven design approaches have been introduced, integrating mathematical optimization techniques, lightweight cryptographic constructions, and efficient access structures. This systematic review analyses ABE schemes from 2018 to 2023, focusing on number-theoretic foundations, security models, and optimization techniques such as pairing reduction, lattice-based cryptography, outsourced decryption, and Boolean circuit optimization. The study further explores emerging applications in cloud computing, Internet of Things (IoT), blockchain systems, and edge computing environments. A total of 30 research studies are reviewed to identify key trends, including efficiency improvements through elliptic curve operations, revocation mechanisms, quantum-resistant constructions, and heuristic optimization of access policies. The findings indicate that while optimization techniques significantly improve performance, trade-offs remain between security strength, computational cost, and policy expressiveness. Future research directions include post-quantum ABE design, AI-driven optimization of access structures, and lightweight encryption mechanisms for resource-constrained devices.