METHOD FOR HOMOMORPHIC ENCRYPTION OF IMAGES DURING REMOTE CLOUD PROCESSING

Abstract

The master's thesis examines the theoretical foundation and development of an efficient method for accelerating the Discrete Fourier Transform (DFT) using cloud computing for image processing. The key feature of the proposed approach is the use of bitwise modifiers to encrypt individual bits of the binary representation of the signal. This technique enables lightweight and flexible encryption on terminal devices, significantly complicating unauthorized reconstruction of the signal during processing on a remote computing system. A thorough theoretical analysis and experimental modeling were conducted, demonstrating that the developed method achieves a 1–2 orders of magnitude increase in processing speed compared to traditional schemes, while maintaining a high level of data confidentiality. The proposed solution is suitable for secure and efficient image processing in cloud environments, particularly in scenarios with limited computational resources on the terminal side.