Hybrid Privacy-Preserving Model Using Homomorphic Encryption for Secure Multi-Tenant Cloud Analytics

In multi-tenant clouds, data privacy and enabling secure analytics are a major issue. The paper offers a hybrid privacy-preserving approach that integrates homomorphic encryption and access control mechanisms in order to secure sensitive information during computation. The paradigm supports computation on encrypted data without decryption, thus preserving confidentiality in untrusted clouds. The proposed system combines the benefits of homomorphic encryption with tenant-enforced policy, providing secure and scalable analytics between separate tenants with high performance and data integrity. Experimental research depicts the effectiveness of the model in balancing security, computational efficiency, and usability of data, making it suitable for privacy-conscious applications in cloud data analytics.