Slope Selection Regressive Probabilistic Rabin Trapdoor Signcryption based Secure Data Transaction in Fog Computing

The Internet of Things (IoT) has become an integral part of numerous applications, connecting devices to facilitate efficient communication. However, the collection and storage of sensitive information in third-party databases pose significant security risks. Unauthorized access to this data can compromise data confidentiality, integrity, and privacy. Fog computing, operating in conjunction with IoT devices, handles a substantial amount of data and is computationally intensive. The security of fog nodes and IoT devices is paramount due to the potential for malicious exploitation of the data they collect. Cryptography emerges as a crucial method for ensuring data security. This research introduces a novel Slope Selection Regressive Trapdoor Public Key Signcryption (SSR-TPKS) method to enhance data security in fog computing environments. By employing a slope selection regressive function, SSR-TPKS effectively identifies data classes and applies trapdoor public key signcryption for encryption and decryption. Experimental results demonstrate the superiority of SSR-TPKS in terms of data confidentiality, data integrity, and encryption time compared to existing cryptography algorithms. The proposed method achieves a $24 \%$ improvement in data confidentiality and a $45 \%$ improvement in training time. SSR-TPKS offers a robust and efficient solution for securing data transactions between fog users and fog servers, safeguarding sensitive information in the IoT ecosystem.