Enhancing Data Security and Privacy through Classification-Based Adaptive Encryption

In modern computing environments, data security and privacy play a crucial role in protecting sensitive information through the use of encryption methods. Nevertheless, relying on a uniform encryption method for all data encounters constraints in accommodating the diverse nature of information. On the other hand, the adoption of a classification-based approach to cloud data encryption enables the application of more efficient and tailored encryption techniques based on the specific characteristics of the data. In this paper, we explore an innovative classification-based approach to encryption, aiming to optimize security, flexibility, and efficiency. This approach involves classifying the data into different categories based on factors such as confidentiality, data type, and data usage. The classification of data allows for the selection of encryption algorithms, ensuring a higher level of security while maximizing resource utilization. Our simulation results demonstrate that the classification-based encryption strategy outperforms a uniform encryption strategy in terms of both security and efficiency. By utilizing this classification-based approach, users of cloud services can effectively protect their data by encrypting it in a way that best suits its specific characteristics and security requirements while minimizing resource utilization.