Integrating Machine Learning Algorithms with an Advanced Encryption Scheme: Enhancing Data Security and Privacy
Keywords:
Cloud Computing, Fog Computing, DES Algorithm, 3DES Algorithm, AES Algorithm, EncryptionAbstract
Distributed computing is considered one of the most thrilling innovations considering its adaptability and versatility. The fundamental issue that happens in the cloud is security. To conquer the threats to security, another technique called Fog computing is developed. The concerns about privacy and security have become increasingly prominent in Fog, including data analysis and cryptography. One approach that has gained attention is the combination of logistic regression, a popular machine-learning technique, with cryptographic algorithms. This integration offers a powerful solution that addresses both privacy and security challenges. This paper explores the rationale behind the combination of logistic regression and cryptographic algorithms, highlighting their benefits and real-world applications. Here the hybrid combination of the (ECC+AES) algorithm and the application of logistic regression is applied to the data for security and the system's performance is measured in terms of encryption/ decryption time, and avalanche effect. The datasets of various kinds are thought of and applied the encryption method over those datasets, whole information over datasets is by and large precisely encoded and decoded back also. Subsequently, both best and worst-case scenarios among the datasets were analyzed, thus evaluating the suitability of the proposed model in a Fog environment.
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