Quantum-Enhanced Differential Privacy for Graph Data

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the application of quantum error correction (QEC) to enhance differential privacy (DP) for graph data. Traditional DP mechanisms, when applied to graph datasets, often result in substantial utility loss due to the inherent structure and complexity of graph representations. We propose a novel approach that utilizes QEC to mitigate this issue by providing a stronger level of privacy protection during computations performed on the graph. Our framework aims to balance privacy guarantees with data utility, leveraging the principles of quantum mechanics to safeguard sensitive information while enabling meaningful analysis. The core idea is that by detecting and correcting errors introduced during computations, we can significantly reduce the risk of information leakage, thus improving the overall privacy-utility trade-off. We present a theoretical analysis of the proposed method and outline a potential implementation strategy, highlighting the key challenges and future research directions. The primary contribution lies in exploring the feasibility and potential benefits of QEC within the context of DP for graph data, addressing a critical gap in existing privacy-preserving techniques.

DOI: 10.5281/zenodo.22185783

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