Framework for Enhanced Privacy-Preserving Consensus System for Distributed SDN: Redefining Security for the East-West Interface

East-West hand-off of the distributed SDN is found to be of vast importance, as the communication between the controllers is prone to various attacks. Encryption, authentication, and blockchain are some of the distinct approaches proposed for stopping Man-in-the-Middle attacks as well as data injection attacks. Nonetheless, it is still a challenge on how this good balance between security and low latency can be created. There is still a need to explore this area for enhanced security in the SDN context.

Khan and Namin [8] pointed out that the SDN controllers are suffering from increasingly severe security challenges and focus on vulnerabilities that decrease their ability to defend against increasingly more sophisticated threats. It throws light on the necessity for better security mechanisms that ensure the reliability and functionality of the SDN in diversifying network environments.

Table 1. A comparison of East-West interface solutions in distributed SDN

Hoang et al. [9] proposed SINA, a novel East-West interface that provides support for interoperability in a heterogeneous or distributed SDN for platforms, besides its novel consistency algorithm using reinforcement learning, in which the adaptive quorum-based replication provides an optimal consistency and network tradeoff. The system provides a trade-off between consistency and network efficiency with adaptive parameters using reinforcement learning.

Nguyen et al. [10] proposed a technique for integrating Blockchain into SDN architectures for providing solutions to challenges of scalability, reliability, and traceability, which was primarily geared towards large-scale environments of IoT. Additionally, in this paper, by using a proof of authority consensus algorithm with the Merkle tree structure, the proposed design enhances traceability while it vastly decreases latency and gas usage for effective functioning. The experimental analysis clearly verifies the efficiency of the proposed solution for improving the performance, scalability, and reliability of SDN.

A solution of Distributed SDN Control Plane Framework, DSF, for East/West was proposed in the study by Almadani et al. [11], which would enable tackling the issue of synchronization, scalability, or performance issues in a large-scale heterogeneous SDN network. This framework is built upon a standardized data-centric real-time publish-and-subscribe pattern, DDS, which has an effective topology synchronization solution for the controllers. Improved scalability, consistency, and integrity of network security can be better aided by performance test analyses carried out for comparing the performance of DSF-based Network Controllers and solutions based on Atomix.

Alrashede et al. [12] assumed that the security framework using blockchain for the East-West interface of SDN is quintessential for allowing inter-controller communications in a blockchain-based SDN network. This is achieved by integrating Ethereum with specially designed algorithms for the authentication, encryption, and access components of the security framework for decentralized security against spoofing data attacks, eavesdropping, or unauthorized access attacks. Experimental results show that the proposed framework is proficient in safeguarding SDN controllers while preserving high network performance with low latency, thus enabling a resilient and trustworthy solution for SDN infrastructure.

In the study by Lam et al. [13], the application of IBC was tackled by the authors for ensuring the security of the communication between the distributed domains of SDN. It was shown that with the use of IBC, key management is much easier, which is also very helpful in mass deployment. Nevertheless, the deployment of IBC in multi-domains has experienced challenges caused by the severe performance issues it creates.

This was the development of an innovative SDN East-West interface that will be used to seamlessly integrate the fixed and mobile SDN controllers. This is done as a proof of concept that can effectively carry out 5G slicing within Access Transport Networks.

Benamrane et al. [15] tackled the issues of realizing SDN with distributions through the introduction of the Communication Interface for Distributed Control Plane (CIDC). CIDC assists with synchronization, notification, and resource sharing among the distributed controllers, including services such as Firewalls and Load Balancers, for improving security and quality. The trials in practical wide area networks proved a distinct enhancement in the CIDC performance level with respect to the cluster controller topology. The study clearly proves a major advancement in realizing effective and secure SDN architectures with distributions.

Adedokun et al. [16] had proposed an improved version of CIDC, called mCIDC, implemented using the Floodlight controller for enabling communications between WAN network controllers. There are four fundamental modules within mCIDC: data updater, data collector, consumer, and producer. It is a publish/subscribe-based system to maintain consistency across controllers. It uses the unobtrusive Netty framework for reducing resource consumption. Two modes of communication were used by mCIDC: notification and full. Both CIDC and mCIDC have some drawbacks regarding the compatibility issue for different controller types. The problem with both solutions lies in their strong consistency model, which causes performance problems in propagating updates. Table 1 illustrates a comparison of East-West Interface Solutions in Distributed SDN.

The architectural framework suggested provides a strong and privacy-based platform to ensure the security of the East-West boundary of the distributed SDNs. Broadly speaking, the elements include three layers: Privacy Layer, Consensus Mechanism, and Security Enhancements. Highly advanced state-of-the-art cryptographic methods, such as homomorphic encryption methods [25, 26] and Zero-Knowledge Proofs (ZKPs) [27, 28], are used in the Privacy Layer for protecting data privacy and ensuring that it is not accessed unauthorizedly during the communication of the controller.

It is then followed by “Somewhat Homomorphic Encryption” or “Levelled Homomorphic Encryption” with smaller computational complexities relative to the “Fully Homomorphic Encryption” with multiplicative depth support for policy evaluation or verification of states at the remote SDN controller. The computation complexities are much lower than in “Fully Homomorphic Encryption,” as it persists in supporting computation on the encrypted data.

Consensus Mechanism is a new privacy-preserving solution that has demonstrated better capability in performance compared with conventional consensus approaches like Practical Byzantine Fault Tolerance (PBFT) and Raft, especially regarding the verification step of encrypted transactions. Finally, the joint authentication, along with the execution of effective encryption algorithms that are resistant to any type of attack, guarantees the security of the communication channels of this scheme.

A. Workflow Description

It achieves this in different systematic steps that ensure the development of a consensus which is secure as well as efficient for the framework. Each of the controllers would be producing a public/private key pair through the Initialization phase while setting up a secure, encrypted session with other peers. While ensuring security integrity, the Distributed Key Management System is responsible for the management of cryptographic keys in a safe manner by rotating them periodically. Data Protection by safeguarding sensitive data/transactions is carried out homomorphically through the Data Protection phase, such that through consensus processes, protected computations would be feasible without threatening the raw data security. This is despite having to ensure confidentiality by allowing the smooth continuation of operations.

In the Consensus Proposal phase, controllers make proposals for encrypted transactions or policy updates based on their local state and share them with peer controllers through the East-West interface in encrypted form to avoid wire-sniffing.

It must be a verification and validation phase where, upon reception, each controller would need to verify the received proposals using the ZKPs. This makes it possible to cryptographically verify the correctness of proposals without exposing the raw data on which the proposals were based. These verification steps include integrity, validity, and consistency of the proposed updates. The following process is a Consensus Agreement. Through a leader-based or decentralized protocol, an enhanced version of PBFT, consensus on the proposed state is reached. The entire process is private through the use of encrypted voting systems, and agreement is achieved through majority consensus. The Policy Synchronization phase, when an agreement has been reached, decodes the agreed state, if necessary, and applies it to each network policy in order to maintain consistency in network configuration and operational integrity of the overall distributed SDN setting.

Finally, in the Monitoring and Auditing phase, it offers an encrypted audit trail for each and every transaction as well as the consensus result. Regular audits are carried out to look for any anomalies or violations that would further improve the security position of the framework. The description in English is given below, unfolding the framework design and various phases of workflow through their mathematical models.

B. Mathematical Model for the Proposed Framework

It represents a model that mathematically models the Privacy Layer, Consensus Mechanism, and Security Enhancements, focusing on the East-West interface secure and efficient operability in distributed SDNs.

1. Definitions and Notations

• Controllers: Ci, where i = 1, 2,…..,n.
• Transactions: Tj where j = 1, 2,…..,m.
• Keys:

k_i^pub: Public key for controller Ci.

k_i^priv: Private key for controller Ci.

• Encryption: Enc(x, K): Encrypts data xx with key K.
• Decryption: Dec(y, K): Decrypts data yy with key K.
• ZKPs: ZKP(P): Validates proposition P without revealing sensitive information.
• Consensus State: S: Represents the agreed system state.
• Audit Log: L: A sequential record of validated transactions.

2. Initialization Phase

Each controller generates a key pair:

$k_i{ }^{p u b}, k_i{ }^{p r i v} \sim$ GenerateKeyPair()

Secure communication channels are established between controllers:

Channel(Ci,Cj) = Secure(k_i ^pub, k_i ^priv)

Key rotation ensures periodic updates for security:

RotateKeys(t)→ k_i ^pub ′, k_i ^priv ′

3. Data Protection Phase

Sensitive transactions are encrypted using homomorphic encryption: T_j^enc = Enc(T_j,K_enc)

Here, K_enc is a shared encryption key, allowing computations on encrypted data without decryption:

Compute(Enc(T_j,K_enc)) = Enc(Compute(T_j),K_enc)

4. Consensus Proposal Phase

Each controller proposes encrypted updates based on its local state: P_i^enc = Enc(P_i, k_i^pub)

These proposals are shared securely via encrypted communication: Transmit (P_i^enc, Channel(Ci,Cj))

5. Verification and Validation Phase

Proposals are validated using ZKPs:

Validate(P_i^enc)→ZKP(Valid(P_i))

This ensures correctness, consistency, and integrity without exposing raw data.

6. Consensus Agreement Phase

Consensus is achieved using an enhanced protocol:

S = Consensus (P₁^enc, P₂^enc,…,P_n^enc)

Voting is conducted using encrypted decisions:

Vote(Ci) = Enc (Decisioni, K_i^pub)

The final consensus state S is determined when the majority agrees:

Majority(Vote(C1),…,Vote(Cn)) = S

7. Policy Synchronization Phase

The agreed state SS is decrypted and applied across controllers: S^dec = Dec(S,K_enc)

This ensures uniform updates to policies throughout the network.

8. Monitoring and Auditing Phase

Each transaction and consensus decision is logged securely:

L←Append (T_j^enc, ZKP(Valid(T_j)),S)

Regular audits validate the logs for anomalies or breaches:

Audit(L)→Detect(Anomalies)

Table 2 below shows the stages involved in the system concerning the mathematical representation and the objectives that should be met to highlight the process involved in ensuring a secured and efficient function within the distributed SDN environment. It also demonstrates the possible balance that can be achieved between the goals of privacy, security, and performance using this framework.

This model details all aspects of the proposed framework with minute detail, with the primary focus being the functioning of the proposed framework in such a way that it ensures very high degrees of privacy, security, as well as efficiencies.

Table 2. Phases in secure distributed SDN coordination

C. Key Features and Advantages

The proposed framework offers several key benefits:

- Improved Privacy: Homomorphic encryption, for example, allows the framework to compute on the encrypted data without the dangers of decryption, while ZKPs ensure that the proof of validation does not leak any information.

- Improved Security: Both sides of authentication in the model ensure the authenticity of the controller’s identity, while the encryption of communication channels inhibits any person from gaining access to or altering data. Regular key rotation prevents any possibility of the integrity of cryptographic techniques being compromised.

- Threat Model: an adversary with the ability to target the East-West communication link both passively and actively is assumed by the suggested framework.

The attacker can:

• Listen on controller-to-controller communications;
• Alter, replay, or insert malicious messages;
• Pretend to be a genuine controller;
• Compromise a subset of controllers displaying arbitrary Byzantine behaviours.

In accordance with traditional BFT assumptions, we take into account up to $\left(f<\frac{n}{3}\right)$. It is possible for controllers to be hacked. The framework assumes a partially synchronous network. When more than a third of the controllers start acting in a Byzantine manner, it allows the isolation of such entities and initiation of a dynamic reconfiguration process by identifying inconsistencies based on the encrypted audit trail and failures in ZKP proofs. This method keeps rogue controllers from affecting consensus outcomes while maintaining partial system availability.

The network is assumed to be somewhat synchronous in the model. The security objectives are:

• Integrity, which guarantees the accuracy of consensus outcomes;
• Confidentiality, which ensures no sensitive controller state is revealed;
• Availability: Preserving control-plane functionality in the face of constrained Byzantine failures.

Homomorphic encryption, ZKPs, mutual authentication, encrypted channels, and periodic key rotation are all used to accomplish each security objective.

Optimized Performance: Light weight cryptography does not significantly contribute to latency or computation costs. A test that came from this found that a possible 30% latency reduction and 25% throughput improvement could be achieved relative to that of the traditional consensus process.

It is designed in such a way that it can be easily integrated with the existing SDN controllers without any major modifications. This framework is scalable, as it is designed in a modular manner that could work effectively in a network with a large number of controllers. This distributed framework is capable of handling dynamic network conditions and also enhances its capabilities of fault tolerance.

To evaluate the performance of the proposed framework, a series of experiments was conducted using a controlled distributed SDN environment. The experiments aimed to measure improvements in privacy leakage, attack success rates, latency, and throughput compared to traditional consensus mechanisms such as PBFT and Raft. The results provide quantitative evidence of the framework's effectiveness in enhancing privacy, security, and performance.

Table 3. Software tools and their functions

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Figure 2. The design of distributed SDN networks and privacy leakage

This section shows the configuration of distributed SDN in GNS3as a test bed. Table 3 lists the software that is used for this section. This software is run on a HP DESK-TOP-F5259A5, with a Processor Intel (R) Core (TM) i7-10750H CPU @2.60GHz, RAM 16.0GB. GNS3 is used as the environment on which all other software is set up. The Ubuntu-servers type 18.04.3LTS are used, an OpenDayLight controller is installed through the configuration of a custom network topology using Mininet [29, 30]. The Python script used to build a custom topology in Mininet and the applications of the OpenDayLight controller. This topology is remotely controlled by three ODL controllers. All data networks make use of Open Flow Switches (OVS switch 1.3), as shown in Figures 2 and 3.

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Figure 3. The design of the distributed SDN network in GNS3

Privacy leakage, the latter, was investigated based on the possibility of sensitive data disclosure during the consensus process. The serious vulnerabilities of the traditional approaches, such as PBFT and Raft, led to a high privacy leakage rate of 20%. On the contrary, the proposed framework eliminates all kinds of privacy leakage by employing advanced cryptographic techniques such as homomorphic encryption and ZKPs. These ensure that sensitive data will not be disclosed during the whole computation and verification process due to its encrypted form.

Attack Success Rate proposed a framework that was tested against simulated cyberattacks aimed at the East-West interface and measured the probability of successful tampering or disruption. Traditional methods have resulted in a 10% attack success rate, due to very limited encryption and authentication mechanisms. However, the proposed framework has reduced the attack success rate to 1%. This is attributed to the aspect of mutual authentication and effective encryption schemes, as well as the rotation of cryptographic keys. It enhances the robustness of the framework to cyber-attacks.

Latency in this work is regarded as the time taken per round for a consensus. All the previous approaches averaged a 100-millisecond latency owing to the computational costs of their respective consensus protocols. In contrast, the suggested framework reduced the latency by 30%, with an average of 70 ms. This is attributed to the suggested framework that incorporates miniaturized cryptographic approaches such as the use of optimal homomorphic encryption parameters with low multiplicative depth and fast digital signature schemes for verification of authenticity, as well as efficient symmetric encryption for secure message transport. These schemes ensure the maximum degree of security with lower processing expenses each round, which do not increase the time for processing and are more secure. The end-to-end average consensus latency is shown in Table 4, which has components of proof of cryptographic work, validation, voting, and band widths. Based on the analysis, it is noted that the proposed framework offers a total of 30% reduction in latency with respect to the test implementation of PBFT.

Table 4. Latency breakdown of the proposed privacy-preserving consensus framework

Table 4 illustrates that gains are mostly seen in the delay of consensus voting and cryptographic processing, while transmission overhead is almost constant. End-to-end latency drops from around 100ms in the baseline system to 70ms in the suggested framework due to the combined effects of fewer validation steps, batching of encrypted processes, and parallel verification. This demonstrates that workflow improvement, not weakening, is what causes the delay decrease.

Throughput, that is, the number of transactions processed per second, was evaluated. In the proposed framework, this was 1000 transactions per second, while both PBFT and Raft had a throughput of 800 transactions per second, representing a 25% increase in throughput due to the efficiency of the optimized consensus protocol of the framework, which reduced latency, as depicted in Table 5.

Table 5. Comparison of traditional methods and the proposed framework

We assessed the system in five configurations: baseline PBFT, PBFT with HE only, PBFT with ZKP only, PBFT with HE + ZKP, and the whole framework, to determine the contribution of each component. The findings demonstrate that while batching and lightweight symmetric encryption enhance latency and throughput, HE and ZKP considerably lower privacy leaks and attack success rates. The ablation results in Table 7 show that eliminating any significant component causes a discernible decline in either security or performance, even if each component contributes in a different way. As a result, every element in the suggested design is justified, even if the study also identifies possible trade-offs for implementation in settings with limited resources.

Table 7. Ablation study of any major component of the proposed privacy preserving consensus framework