Anomaly detection is a critical component in ensuring the security and integrity of data in cloud computing environments. However, traditional anomaly detection methods often require data to be shared in unencrypted form, raising privacy concerns for cloud users. To address this challenge, this paper proposes a novel approach that enhances privacy in cloud anomaly detection using lightweight homomorphic encryption. The proposed method allows for the detection of anomalies in encrypted data without the need to decrypt it, thereby preserving the confidentiality of sensitive information. The study evaluates the effectiveness and efficiency of the proposed approach through experimentation and demonstrates its potential to provide privacy-preserving anomaly detection capabilities in cloud environments. By leveraging lightweight homomorphic encryption, cloud users can confidently utilize anomaly detection services while maintaining the confidentiality of their data.

In the realm of complex systems, ensuring robustness against failures and disruptions is paramount. Distributed redundancy, a strategy involving the replication of functions or components across multiple nodes, has emerged as a powerful approach to enhance system resilience. This study investigates the role of distributed redundancy in bolstering the robustness of complex systems against various challenges, such as hardware failures, cyberattacks, and environmental uncertainties. Through theoretical analysis and empirical case studies, this research explores the benefits, trade-offs, and optimal implementation strategies of distributed redundancy. The findings shed light on the potential of this approach to fortify the stability and functionality of intricate systems in the face of adversity.