Differential Privacy Preserving Distributed Quantum Computing

• URL: http://arxiv.org/abs/2412.12387v2
• Date: Mon, 06 Jan 2025 07:05:11 GMT
• Title: Differential Privacy Preserving Distributed Quantum Computing
• Authors: Hui Zhong, Keyi Ju, Jiachen Shen, Xinyue Zhang, Xiaoqi Qin, Tomoaki Ohtsuki, Miao Pan, Zhu Han,
• Abstract summary: This paper introduces a novel concept called quantum R'enyi differential privacy (QRDP) Based on the new quantum R'enyi divergence, QRDP provides delicate and flexible privacy protection by introducing parameter $alpha$. We analyze a variety of noise mechanisms that can implement QRDP, and derive the lowest privacy budget.
• Score: 33.23640077992224
• License:
• 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.

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