VDNABDS, A DNA-Based Cryptographic Protocol for Enhancing Cloud Security

Summary

The goal of the VDNABDS protocol is to enhance cloud security by using DNA-based encryption techniques to generate fast, unbreakable keys. It aims to protect sensitive data against brute-force and quantum attacks while ensuring high performance, scalability, and seamless integration with modern cloud systems.

Abstract

Modern cloud storage systems often struggle to balance security and performance-strong encryption tends to slow down operations, while faster solutions can compromise data safety. To solve this, we developed Variational DNA-Based Data Security (VDNABDS), a next-generation encryption system inspired by the biological structure of DNA. Instead of relying solely on traditional mathematical algorithms, this method converts user-specific information into DNA-like sequences using the four nucleotides (A, T, C, G), then applies smart shuffling and transformation techniques to secure cloud-stored files. This method blends dynamic key generation with bio-inspired patterns, achieving rapid encryption without sacrificing protection. In testing, this method generated secure keys in just 5 ms, which is 15 times faster than existing models like Cloud Security with Dynamic Encryption Sequences (CSDES), and completed full encryption in 4 s, even under high user loads of nearly 1,000 simultaneous users. The proposed Method also provides exceptional defense against cyber threats, offering 1 x 10³⁸ unique key combinations-making brute-force and quantum attacks practically impossible. Its adaptive design constantly updates security patterns, making it highly resilient to intrusion. Importantly, it integrates smoothly with existing cloud platforms, enabling fast data access while maintaining strong privacy safeguards. Real-world experiments showed VDNABDS consistently outperforms traditional encryption models in both speed and reliability. With its robust, scalable, and hardware-independent architecture, this system is especially suited for industries like healthcare, finance, and defense, where data sensitivity is paramount. Looking ahead, we aim to expand this biological encryption model to smartphones and Internet of Things (IoT) devices, paving the way for a new era of fast, secure, and quantum-resistant data protection.

Introduction

Cloud computing has become essential for modern data services, offering flexibility, scalability, and efficiency. However, with this widespread adoption comes increased exposure to cyber threats, especially those targeting data confidentiality and integrity. Traditional encryption algorithms such as AES-256 and RSA, though widely used, face growing limitations. These methods require heavy computational resources and are vulnerable to evolving technologies like quantum computing¹. This creates an urgent need for novel encryption systems that are lightweight, scalable, and future-proof.

DNA-based cryptography has emerged as a promising alternative due to its inherent complexity, randomness, and potential for parallel computation². However, the theoretical appeal of these methods often clashes with their practical implementation. Most existing DNA-based schemes have struggled with real-world applicability as they are often hardware-dependent, require specialized laboratory equipment, or lack the performance and scalability needed for dynamic cloud environments³. These limitations have created a significant gap between the theoretical promise of bio-inspired security and its practical applicability.

To address this, we present Variational DNA-Based Data Security (VDNABDS)-a software-based encryption framework that transforms user-specific inputs into dynamic DNA-like keys using SHA-256 and XOR operations. The method enables key generation in under 5 ms and encrypts large data volumes in just 4 s, significantly outperforming earlier models like CSDES and ZMCACM⁴. VDNABDS supports over 1 x 10³⁸ unique key combinations, providing strong protection against brute-force and quantum attacks.

While many researchers have explored solutions to cloud security, they often focus on specific, isolated problems. For instance, Wang et al.⁵ proposed a secure authentication model for cloud computing, but it lacks content-level encryption capabilities. Similarly, Ahmed et al.⁶developed DNACDS for IoE environments, yet the scheme suffers from limited scalability in real-time testing. Other efforts combine Blowfish with blockchain⁷or apply DNA for access control^8, but they often fall short in performance or adaptability. VDNABDS fills these gaps with its fast, hardware-independent, and session-specific encryption strategy, which is validated using CloudSim with a large dataset and concurrent users.

In summary, this work provides the following key contributions to the field of cloud security and DNA-based cryptography. We introduce VDNABDS, a novel, software-only cryptographic framework that transforms user-specific information into dynamic DNA-like encryption keys. We demonstrate exceptional performance and scalability, with VDNABDS achieving key generation in just 5 ms and encrypting a 3GB dataset in 4.1 s, outperforming existing models like ZMCACM and AES-256. We validate the protocol's post-quantum security by demonstrating a 1024-bit DNA-derived key and non-algebraic transformations, which provide a security level well beyond the NIST threshold and resist both Shor's and Grover's algorithms⁹. We propose a dual-layer key protection scheme combining Elliptic Curve Cryptography (ECC) and RSA-OAEP to securely wrap the DNA key, enhancing its resistance against brute-force and replay attacks¹⁰.

Protocol

This research did not involve human participants, animals, or the use of biological samples. All testing and evaluation were carried out with artificially generated data, including randomly created identifiers such as MAC addresses, dates of birth, and password strings. No personal or sensitive information was collected, retained, or examined at any stage. Within the VDNABDS protocol (Figure 1), input-based keys were restricted to synthetic, non-traceable values, serving only to illustrate performance and functional outcomes in a controlled setting. Every step of the study was conducted in accordance with institutional policies on cybersecurity experimentation and ethical data handling, ensuring alignment with international standards for privacy protection and responsible research practices.

1.System preparation

1. Install required tools: Install a cloud simulation toolkit in a Java environment using a supported JDK version (e.g., version 8 or later). Use any integrated development environment (IDE) compatible with Java to run the project.
2. Configure the system environment . Set up a Python environment (version 3.8 or later) with standard libraries for hashing, random generation, and binary operations. Create a directory structure with folders named input_data, dna_keys, and encrypted_output.
3. Define salt and CRC modules . Initialize a 128-bit system-wide salt value and a cyclic redundancy check (CRC32) function. These should be stored securely in system memory.
4. Set cryptographic parameters. Use a standard elliptic curve, such as secp384r1, for asymmetric encryption. Configure RSA-OAEP with a 4096-bit key size for public-key encryption.

2. Smart key generation using DNA mapping

1. Collect user-specific inputs. Gather the user's MAC address, date of birth (in DD-MM-YYYY format), and a strong password.
2. Concatenate and hash inputs. Concatenate the user-specific inputs and apply a SHA-256 hash. Truncate the hash to 128 bits and convert it to binary.
   Example Input:MAC: 00-1B-44-11-3A-B7DOB: 15-08-2000Password: StrongP@ssword123SHA-256 Hash (Truncated to 128 bits): b7e23ec29af22b0b4e41da31e868d572
3. Expand the binary sequence. XOR the 128-bit hash (H) with a 128-bit salt (S) to produce the XORed result (X). X=HS
   Example Salt: e3f3cd1a49d20a7c3b8abf243e7211e8
   Append a CRC32 checksum (C) and an 864-bit random pad (P) to form the final 1024-bit binary sequence (R). R=X||C||P
4. Convert binary to DNA sequence. Use the binary-to-DNA mapping: 00 → A, 01 → T, 10 → C, 11 → G. Apply the mapping to convert all 1024 bits into a DNA sequence.
   DNA Sequence (First 20 Bases Example): ATGCCTTAGGTAGGCTATAC
5. Shuffle the DNA sequence. Use a time-seeded Fisher-Yates shuffle algorithm to randomize the DNA sequence. Seed the shuffle using the SHA-256 hash of the system timestamp and session ID.

3. DNA-based encryption key formation

1. Segment and transform. Split the shuffled DNA sequence into four equal 256-bit segments: S1, S2, S3, S4. Apply a DNA-XOR operation to create two key segments, K1 and K2, as defined by the following relations: K1=S1S2 K2=S3S4
2. Assemble the final DNA encryption key. Combine the XOR outputs to form the final DNA encryption key (DNADK): DNADK=K1||K2

4. Dual-layer key encryption and secure storage

1. Apply elliptic curve encryption. Encrypt the DNADK using elliptic curve cryptography with the data owner's private key.
2. Apply RSA-OAEP encryption. Encrypt the ECC-encrypted result using RSA-OAEP with the recipient's public key.
3. Store the encrypted DNA key. Save the dual-encrypted DNADK in the secure dna_keys directory.

5. Data encryption and upload to cloud storage

1. Convert plaintext to binary. Split the original file into binary blocks of equal length.
2. Encode binary blocks into DNA format. Apply the binary-to-DNA conversion described in step 2.4 to the binary blocks.
3. Shuffle and apply DNA-XOR. Shuffle and transform each DNA-encoded block using the DNA-XOR logic defined in step 3.
4. Apply error-correction and finalize encryption. Add Reed-Solomon error-correcting codes to the DNA-encoded data. Store the final encrypted output in the encrypted_output directory.
5. Upload encrypted data to the cloud. Transfer the encrypted DNA-encoded files to the designated cloud service provider and log the upload with relevant metadata.

6. Data access and secure decryption

1. Authenticate the user. Use multi-factor authentication mechanisms to validate user access.
2. Retrieve the encrypted DNA key. Access the encrypted DNADK from the secure key directory.
3. Perform key decryption. Use the recipient's private RSA key, followed by the data owner's ECC private key, to decrypt the original DNADK.
4. Reconstruct original data. Apply the reverse DNA mapping, DNA-XOR decoding, and error-correction decoding to recover the original plaintext data.

7. Optimized user identifier handling

1. Classify user identifiers based on length and allocate them into designated index groups.
2. Accelerate lookups by leveraging indexed hash tables together with caching strategies.
3. Authenticate users by verifying their identifiers against pre-computed cryptographic hashes before access is permitted.

8. Safety, security, and compliance measures

1. Protection of DNA-derived keys: Store DNADKs exclusively in encrypted locations with strict access permissions.
2. Secure cleanup of temporary data: Remove intermediate files and buffers through certified secure-erase methods.
3. Session-specific keying: Create a distinct DNADK for every session to preserve forward secrecy.

9. Testing and validation (Figure 2)

1. Simulation setup: Implement the protocol within a CloudSim 3.0.3 environment configured to support up to 1,000 users simultaneously and a dataset of 3 GB.
2. Performance indicators: Track values such as key generation latency, encryption/decryption time, memory requirements, entropy levels, and scalability under varying loads.
3. Comparative experiments: Evaluate VDNABDS alongside AES-256 + RSA-OAEP, ZMCACM, CSDES, and RDIS under equivalent conditions.
4. Scalability study: Gradually raise the number of concurrent users (100, 500, 1,000) while recording encryption and decryption performance.
5. Security assessment: Estimate resistance to brute-force by measuring effective key space and entropy; also, examine theoretical robustness against Shor's and Grover's quantum algorithms.
6. Resource-limited trials: Run lightweight variants on a Raspberry Pi 4 (1.5 GHz quad-core CPU, 2 GB RAM) to test applicability for IoT-type scenarios.
7. Documentation and reproducibility: Preserve experiment outputs, logs, and performance reports to support independent validation and replication.

Results

The proposed VDNABDS protocol was evaluated through a series of simulated cloud environment experiments to assess its performance against both traditional and other DNA-based cryptographic methods. Evaluation metrics included key generation time, encryption and decryption speeds, key entropy, and scalability under varying user loads¹¹.

Performance comparison with existing schemes

Discussion

The emergence of DNA-based encryption has opened a novel frontier in post-quantum cryptography. However, real-world adoption has lagged due to limitations in reproducibility, scalability, and hardware dependence. Techniques such as those proposed by Ahmed et al. and Sharma et al. addressed certain cloud vulnerabilities using DNA and blockchain models but lacked dynamic key handling and showed limited resilience to high concurrency environments^18,19. VDNABDS overc...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
JAVA	Oracle
CloudSim	Github
Python	Python software foundation