Publication

Abstract : Fog computing offers a viable solution to the overwhelming demands placed on cloud-based medical data processing, addressing issues related to low latency, storage, and bandwidth optimization for IoT applications. However, fog computing also presents challenges, particularly concerning privacy and security. Attribute-based encryption (ABE) has emerged as a robust cryptographic technique for ensuring the secure sharing of medical data. Despite its potential, existing systems face unresolved issues such as collusion induced by compromised users, trusted third-party attacks, bottleneck concerns in key generation, and inefficient key distribution. This study introduces both user and file-specific attributes-based encryption. It incorporates Chebyshev chaotic map to generate hash functions, enabling efficient session-based and mutual authenticated key setup in fog-based data-sharing scenarios. The proposed method offers a significant benefit through its authenticated session management, ensuring that confidential data is shared exclusively with authenticated users. Moreover, the approach effectively reduces third-party processing overheads and enhances protection against diverse cryptographic attacks like sybil attack, reply attack, guessing attack and modify attack. The proposed method effectively addresses crucial security aspects like authenticity, integrity and unpredictability. The comparison of computational and communication times with current systems underscores the significance of integrating ABE and the Chebyshev chaotic map for data sharing on devices with limited resources.