Privacy Layer

How do you prove a dataset is worth $50,000 without showing it to anyone?

RONNE combines two breakthrough cryptographic technologies to solve this fundamental marketplace challenge. Zero-knowledge proofs verify data quality without exposure. Homomorphic encryption enables training without data access.

Zero-Knowledge Proofs: Verification Without Exposure

Our zk-SNARKs implementation uses Circom circuits with the Groth16 proving scheme, optimized for blockchain efficiency. Custom circuit designs let data providers prove statistical distributions, quality metrics, and compliance status without revealing underlying information.

Consider statistical verification: hospitals can prove their cancer dataset contains balanced demographics across age groups, ethnicities, and cancer types. AI developers receive mathematical certainty about diversity without seeing individual patient records. Circuit optimizations generate these proofs in under 30 seconds for million-record datasets.

Quality attestation circuits demonstrate accuracy, completeness, and timeliness through aggregated scores. Pharmaceutical companies prove their clinical trial data meets FDA standards. Medical device manufacturers demonstrate sensor data accuracy. All without exposing methodologies or individual measurements.

Distribution verification enables demographic, geographic, or temporal diversity proof. Autonomous vehicle companies can demonstrate their sensor data covers diverse weather conditions, road types, and traffic scenarios without revealing specific routes or locations.

Proof batching and recursive composition minimize computational overhead while maximizing verification capabilities. Universal trusted setup eliminates complex ceremony requirements for data providers.

Homomorphic Encryption: Training in the Dark

RONNE's FHE leverages the CKKS scheme for efficient encrypted floating-point computation. Neural networks train entirely within encrypted space.

Gradient calculations, backpropagation, and parameter updates occur on encrypted data. Specialized polynomial approximations replace activation functions with homomorphically evaluable alternatives. Model parameters remain encrypted until data owners authorize release.

Performance optimizations achieve training speeds within 5-10x of plaintext methods. GPU implementations and custom FHE silicon acceleration make this commercially viable. Bootstrap management handles extended training sessions automatically.

Distributed computing enables enterprise-scale workloads while maintaining security guarantees across multiple nodes.