Privacy-Preserving Network Analytics and Virtual Network Functions in SDN and Cloud Environments Using Homomorphic Encryption

The rapid adoption of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) in multi-tenant cloud and ۵G/۶G environments has improved programmability and service flexibility, yet it has simultaneously intensified privacy risks. Centralized controllers and cloud-hosted Virtual Network Functions (VNFS) routinely process sensitive flow statistics and metadata under an honest-but-curious threat model, while end-to-end encryption (especially TLS ۱.۳) and regulations such as GDPR further constrain traditional inspection techniques. Existing privacy solutions differential privacy, trusted execution environments, and secure multi-party computation either degrade analytical utility, introduce hardware-specific trust assumptions, or impose prohibitive communication overhead. This paper presents a unified framework that applies the Cheon Kim Kim Song (CKKS) fully homomorphic encryption scheme to enable both privacy-preserving network analytics and core virtual network functions without decryption at intermediate entities. The three-layer architecture (Data Plane, SDN Control Plane, Cloud Analytics/VNF Platform) supports encrypted flow-statistics aggregation, traffic classification, anomaly detection, load-balancing decisions, policy/firewall evaluation, and multi-tenant VNF orchestration. Optimized CKKS encoding, ciphertext packing, noise management, and integration with existing SDN northbound interfaces are detailed. Security analysis confirms semantic security under the Ring Learning With Errors assumption against honest-but-curious adversaries. Although computational overhead remains a practical limitation for ultra-high-speed settings, the framework demonstrates that strong confidentiality can be achieved with acceptable accuracy and functional correctness, offering a viable path toward privacy-aware SDN-cloud deployments.

کلیدواژه ها:

Homomorphic encryption ، CKKS ، Software-defined networking ، Network function virtualization ، Privacy-preserving analytics ، Virtual network functions