Page No: 415-426
Abstract : The exponential growth of cloud storage has necessitated advanced security
measures to protect sensitive data from unauthorized access. Traditional encryption methods
provide a layer of security, but they often lack the robustness needed to address emerging
threats. This paper introduces an optimized framework for secure cloud storage that integrates
data encryption, decryption, and dispersion using cutting-edge optimization techniques. The
proposed model enhances data security by first encrypting the data, then dispersing it across
multiple cloud servers, ensuring that no single server holds the complete dataset. Decryption
occurs only when the dispersed data fragments are reassembled, which adds an additional
layer of security. We also explore various optimization algorithms to improve the efficiency of
encryption and dispersion processes, thereby reducing computational overhead while
maintaining high security. The implementation of this framework is evaluated on multiple cloud
platforms, demonstrating its effectiveness in safeguarding data with minimal performance
impact. Future enhancements may include integrating machine learning algorithms to predict
and adapt to new security threats in real time, further solidifying the reliability of cloud storage
solutions.