NCorr-FP: A Neighbourhood-based Correlation-preserving Fingerprinting Scheme for Intellectual Property Protection of Structured Data

TL;DR

NCorr-FP is a novel neighbourhood-based fingerprinting method for structured data that preserves statistical fidelity, maintains data utility, and is robust against various attacks, ensuring effective intellectual property protection.

Contribution

The paper introduces NCorr-FP, a new correlation-preserving fingerprinting scheme for structured data that balances fidelity, utility, and robustness, addressing limitations of existing methods.

Findings

High imperceptibility with minimal divergence measures.

Achieves 100% detection confidence under data deletions.

Robust against adaptive and collusion attacks.

Abstract

Ensuring data ownership and traceability of unauthorised redistribution are central to safeguarding intellectual property in shared data environments. Data fingerprinting addresses these challenges by embedding recipient-specific marks into the data, typically via content modifications. We propose NCorr-FP, a Neighbourhood-based Correlation-preserving Fingerprinting system for structured tabular data with the main goal of preserving statistical fidelity. The method uses local record similarity and density estimation to guide the insertion of fingerprint bits. The embedding logic is then reversed to extract the fingerprint from a potentially modified dataset. Extensive experiments confirm its effectiveness, fidelity, utility and robustness. Results show that fingerprints are virtually imperceptible, with minute Hellinger distances and KL divergences, even at high embedding ratios. The system also maintains high data utility for downstream predictive tasks. The method achieves 100\% detection confidence under substantial data deletions and remains robust against adaptive and collusion attacks. Satisfying all these requirements concurrently on mixed-type datasets highlights the strong applicability of NCorr-FP to real-world data settings.