Book Description

"An Enhanced Homomorphic Encryption Model for Preserving Privacy in Clouds" is a comprehensive and innovative book that explores the application of enhanced homomorphic encryption techniques to safeguard privacy in cloud computing environments. Authored by experts in the field, this book serves as a valuable resource for researchers, professionals, and practitioners interested in leveraging advanced encryption methods to protect sensitive data while harnessing the benefits of cloud computing. In this book, the authors delve into the critical need for privacy preservation in cloud computing, where data is outsourced to remote servers. They introduce an enhanced homomorphic encryption model that enables computations on encrypted data, allowing secure and privacy-preserving data processing in cloud environments. The book covers various aspects of the enhanced homomorphic encryption model, including its theoretical foundations, implementation considerations, and practical applications. Key topics covered in this book include: Privacy challenges in cloud computing: The authors provide a comprehensive overview of the privacy concerns associated with cloud computing, including data leakage, unauthorized access, and privacy breaches. They highlight the need for encryption techniques that allow data to remain confidential even when processed in the cloud. Homomorphic encryption fundamentals: The book offers an in-depth exploration of homomorphic encryption techniques and their applications in cloud computing. Readers gain a solid understanding of fully homomorphic encryption (FHE) and its variations, including partially homomorphic encryption (PHE) and somewhat homomorphic encryption (SHE). Enhanced homomorphic encryption model: The authors present their enhanced homomorphic encryption model that incorporates innovative approaches to improve the efficiency, scalability, and security of homomorphic encryption. They discuss techniques such as ciphertext compression, parallelization, and optimization algorithms, ensuring the practicality of the encryption model for real-world cloud computing scenarios. Secure data processing in the cloud: The book explores how the enhanced homomorphic encryption model enables secure and privacy-preserving data processing in cloud environments. It covers various applications, including secure search, data mining, machine learning, and data analytics, demonstrating how encrypted data can be utilized without compromising privacy. Performance considerations and trade-offs: The authors address the performance challenges and trade-offs associated with homomorphic encryption. They discuss factors such as computation complexity, encryption overhead, and key management, providing insights into optimizing the performance of the enhanced homomorphic encryption model. Practical implementation and case studies: The book includes practical implementation considerations and case studies that showcase the deployment and effectiveness of the enhanced homomorphic encryption model in real-world cloud computing scenarios. The case studies cover domains such as healthcare, finance, and sensitive data sharing, illustrating the practicality and benefits of the proposed model. Throughout the book, the authors provide insights, practical examples, and algorithmic explanations to facilitate a deep understanding of the enhanced homomorphic encryption model. By leveraging the power of enhanced homomorphic encryption, "An Enhanced Homomorphic Encryption Model for Preserving Privacy in Clouds" equips its readers with the knowledge and tools necessary to protect sensitive data, preserve privacy, and enable secure cloud-based computations.