Privacy-enhanced data compression using quantum zk-SNARKs and variational auto-encoders in cloud-IoT based healthcare sensor data for medical applications

The increasing rate of growth of the Internet of Things (IoT) in cloud-hospitality health has brought in data storage, transmission, and security challenges with the advent of quantum-enabled threats. Traditional compression methods struggle with computational inefficiency and the threat of invasion of privacy. This paper proposes a Quantum-Enhanced Zero-Knowledge Healthcare Compression Network for solving these challenges by combining Zero-Knowledge Proofs and Quantum-Inspired Deep Learning. The main goal is to provide privacy-preserving, efficient data compression along with optimizing computation costs and safeguarding sensitive healthcare records. Drawbacks in present cryptographic techniques, e.g., high computational costs in homomorphic encryption and scalability limitations in blockchain, require a novelty Adaptive Quantum-Assisted Zero-Knowledge Verification and Quantum Fusion-AutoCNN Encoder (QF-AutoCNN) to overcome this research. This work’s originality lies in combining Quantum zk-SNARKs, Hybrid Quantum Feature Encoding, and Reinforcement Learning-Based Challenge Optimization to provide better security, compression ratio, and verification efficiency. Experimental results show better accuracy (0.9816), improved F-measure (0.9709), and less computational overhead, better than other current methods such as convolutional neural networks-encryption and proxy re-encryption. This research greatly adds to safe cloud healthcare IoT by lessening privacy threats, maximizing storage space, and minimizing processing time, guaranteeing real-time handling of medical information.