Hybrid Dual-Signature Scheme (HDSS) A proof-of-concept project exploring a hybrid post-quantum digital signature scheme that combines Falcon and SPHINCS+ to improve resilience against future quantum attacks in blockchain and IoT environments. This project focuses on design, implementation, and benchmarking, rather than production deployment.

Project Overview With the advancement of quantum computing, classical digital signature schemes such as RSA and ECDSA are expected to become vulnerable. HDSS addresses this risk by aggregating two post-quantum signature algorithms into a single composite signature. The core idea is simple: A signature is considered valid only if all underlying post-quantum schemes verify successfully. This increases security guarantees for long-lived and high-value systems such as blockchains and IoT infrastructures.

Key Objectives Explore hybrid post-quantum signature design for quantum-resilient systems

Compare hybrid signatures with classical and individual PQC schemes

Study security vs performance trade-offs in real-world data scenarios

Key Features Hybrid Dual-Signature Construction Combines Falcon (lattice-based) and SPHINCS+ (hash-based) signatures into a single verification workflow.

Proof-of-Concept Implementation Implemented core key generation, signing, and verification logic using Python and Golang.

Benchmarking Framework Evaluates HDSS against RSA, ECDSA, Falcon, and SPHINCS+ using metrics such as:

Signature validity

Signature size

Signing and verification latency

Tamper Detection Any modification to the signed message causes verification failure, demonstrating strong integrity guarantees in preliminary experiments.

Tech Stack Languages: Python, Golang

Cryptography Focus:

Post-Quantum Cryptography (PQC)

Hybrid digital signatures

Blockchain and IoT security concepts

High-Level Workflow Generate independent key pairs for Falcon and SPHINCS+

Sign the same message using both algorithms

Aggregate both signatures into a single HDSS signature

Verify each component signature independently

Accept the message only if all verifications succeed

Datasets Used Real-world blockchain transaction datasets

IoT and structured CSV-based datasets

These datasets were used to simulate realistic signing and verification scenarios for benchmarking.

Security Considerations Security relies on defense-in-depth: an attacker must break all underlying schemes to forge a valid signature

Designed to remain secure even if one algorithm becomes weaker in the future

Suitable for long-term trust systems where quantum resilience is prioritized

Limitations Proof-of-concept and small-scale benchmarking only

Not integrated into a production blockchain or IoT platform

Higher computational cost and signature size compared to individual schemes

Full-scale deployment and optimization are outside the current scope

Future Work Large-scale benchmarking on constrained IoT hardware

Optimization of signature size and signing latency

Formal security proofs and side-channel resistance analysis

Integration with blockchain execution layers and secure middleware