Secure Data Federation and Analytics through Homomorphic Encryption in Multi-Tenant Cloud Environments

Abstract

This work proposes an end-to-end architecture for secure data federation and privacy-preserving analytics across multi-tenant cloud environments using homomorphic encryption (HE). We address the core challenge of enabling cross-tenant joins, aggregations, and model scoring without exposing plaintext or weakening tenant isolation. The framework integrates schema-level federation with encrypted data lakes, columnar ciphertext packing for vectorized operations, and an adaptive HE planner that selects between CKKS for approximate analytics and BFV/BGV for exact computations. To bound latency while maintaining correctness, we apply batching, ciphertext relinearization, and rotation scheduling, and offload heavy primitives to accelerator-ready microservices. Policy-aware orchestration enforces per-tenant keys via cloud KMS and supports fine-grained access control and revocation. For sensitive workflows, we compose HE with complementary protections secure enclaves for control-plane logic, differential privacy on result releases, and zero-knowledge proofs to attest query policy compliance achieving defense-in-depth without collapsing the HE trust model. The system exposes SQL-like and DataFrame APIs, a query optimizer that estimates noise budgets and bootstrapping costs, and lineage-rich audit trails for regulatory reporting. We outline deployment patterns on containerized clusters, discuss cost/performance trade-offs under realistic workloads, and provide guidance on tenancy hardening (noisy neighbor resistance, side-channel hygiene). The result is a practical pathway for organizations to collaborate on analytics and machine learning across clouds and jurisdictions while preserving confidentiality, minimizing data movement, and meeting compliance obligations