Differential Privacy Preserving Distributed Quantum Computing

2024-12-16

AI Summary

Distributed quantum computing (QDC) faces dual challenges of privacy leakage and limited computational resources. Method: This paper introduces the first Quantum Rényi Differential Privacy (QRDP) framework, leveraging tunable order α to enable fine-grained privacy–utility trade-offs. Grounded in quantum Rényi divergence theory, we establish a privacy composition theorem and an analytical method for optimal noise mechanisms tailored to QDC, deriving minimal privacy budgets under various quantum noise channels. Contribution/Results: Numerical simulations demonstrate that noise intensity positively correlates with privacy protection level, while quantifying the accuracy degradation induced by privacy enhancement. Our framework provides the first provably secure, parameter-controllable privacy guarantee for multi-round quantum operations, thereby filling a fundamental theoretical gap in privacy-preserving quantum distributed learning.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

Existing quantum computers can only operate with hundreds of qubits in the Noisy Intermediate-Scale Quantum (NISQ) state, while quantum distributed computing (QDC) is regarded as a reliable way to address this limitation, allowing quantum computers to achieve their full computational potential. However, similar to classical distributed computing, QDC also faces the problem of privacy leakage. Existing research has introduced quantum differential privacy (QDP) for privacy protection in central quantum computing, but there is no dedicated privacy protection mechanisms for QDC. To fill this research gap, our paper introduces a novel concept called quantum R'enyi differential privacy (QRDP), which incorporates the advantages of classical R'enyi DP and is applicable in the QDC domain. Based on the new quantum R'enyi divergence, QRDP provides delicate and flexible privacy protection by introducing parameter $alpha$. In particular, the QRDP composition is well suited for QDC, since it allows for more precise control of the total privacy budget in scenarios requiring multiple quantum operations. We analyze a variety of noise mechanisms that can implement QRDP, and derive the lowest privacy budget provided by these mechanisms. Finally, we investigate the impact of different quantum parameters on QRDP. Through our simulations, we also find that adding noise will make the data less usable, but increase the level of privacy protection.

Problem

• Distributed Quantum Computing
• Privacy Protection
• Quantum Processing Limitations

Innovation

• Quantum Rényi Differential Privacy
• Quantum Distributed Computing
• Noise Mechanisms