Efficient Chaos Based Homomorphic Permutation and SPM-BOW Algorithm for Secure Image Storage and Retrieval in Cloud

Storing digital images in the cloud, managed by third-party services, offers numerous advantages over traditional on-premises storage. It empowers scalability, accessibility from anywhere with internet, reliability through redundancy, cost-effectiveness with pay-as-you-go models. Given the rising reliance on cloud storage and mounting concerns regarding data privacy, a pioneering technique to securely retrieve images within cloud environments. The proposed approach focuses on safeguarding sensitive image content while ensuring compliance with privacy regulations. By leveraging advanced encryption techniques, this approach safeguards image data prior to its transmission to cloud servers, effectively reducing the likelihood of unauthorized access and potential data breaches. The technique involves extracting features directly from encrypted files, ensuring streamlined image retrieval while upholding user privacy and maintaining the integrity of the encrypted data.By applying discrete cosine transform (DCT) coefficients and sophisticated encryption techniques, image content is obscured to ensure confidentiality. Local features extracted from encrypted big-blocks serve as the foundation for image representation. Leveraging these features, a feature vector is constructed using the Spatial Pyramid Matching and Bag of Words (SPM – BOW) model, facilitating efficient image retrieval while maintaining privacy. The proposed framework significantly advances cloud storage solutions by achieving 91.8% accuracy in image retrieval, surpassing the performance of other existing retrieval algorithms in terms of security and efficiency.