With the increasing adoption of cloud computing, ensuring data privacy and security has become a critical challenge, particularly in the context of anomaly detection. This study proposes a lightweight homomorphic encryption-based approach to enhance cloud security by enabling privacy-preserving anomaly detection. Homomorphic encryption allows computations to be performed on encrypted data without the need for decryption, ensuring that sensitive information remains protected throughout the detection process. The paper explores the implementation of a lightweight encryption scheme that balances both computational efficiency and strong security, making it suitable for real-time anomaly detection in cloud environments. The proposed method is evaluated against traditional encryption approaches, demonstrating its capability to detect anomalous behaviors without exposing raw data to cloud service providers. Results indicate that the lightweight homomorphic encryption method maintains high levels of accuracy in detecting anomalies while ensuring minimal performance overhead, making it a promising solution for secure cloud-based anomaly detection systems. This work contributes to advancing privacy-preserving techniques in cloud security and paves the way for more secure and efficient cloud computing applications.

Cloud security, Anomaly detection, Homomorphic encryption

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