A Novel Framework for Channel Security Using Hybrid Attribute-Based Encryption with Privacy Optimizations

Abstract

In the evolving landscape of secure communication, ensuring robust channel security while preserving user privacy remains a critical challenge. This paper introduces a novel framework that leverages hybrid attribute-based encryption (ABE) to enhance channel security with optimized privacy preservation mechanisms. Traditional encryption methods often lack fine-grained access control, while existing ABE-based solutions can suffer from computational overhead and privacy issues. Our framework addresses these limitations by combining ABE with another encryption scheme to achieve secure and efficient data transmission. We incorporate privacy-preserving techniques to minimize user attribute exposure.  The proposed framework integrates the flexibility of attribute-based encryption with the combined efficiency of Fernet and Initialization vector. Exponential differential privacy mechanism is integrated with the proposed framework to provide addition privacy and security of communicated user information. The security analysis demonstrates that the framework offers strong confidentiality, collusion resistance, and unforgeability guarantees. Performance evaluation confirms efficient operation compared to existing solutions. This proposed framework provides a promising approach for secure communication in various applications and obtained optimized in terms of latency, strong attack resistance and less computational overhead.