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arXiv:2312.08210 · International Conference on Quantum Communications, Networking, and Computing (QCNC)

Differential Privacy Preserving Quantum Computing via Projection Operator Measurements

Yuqing Li, Yusheng Zhao, Xinyue Zhang, Hui Zhong, Miao Pan, Chi Zhang

Published Jul 1, 2024• 4 citations
arXivHTMLPDF

Abstract

Quantum computing has been widely applied in various fields, such as quantum physics simulations, quantum machine learning, and big data analysis. However, in the domains of data-driven paradigms, ensuring databases privacy. For classical computing, we can incorporate the concept of differential privacy (DP) to meet the standard of privacy preservation by manually adding noise. In quantum computing scenario, researchers have extended classic DP to quantum differential privacy (QDP) by considering quantum noise. In this paper, we propose a novel approach to satisfy the QDP definition by considering the errors generated by the projection operator measurement, which is denoted as shot noise. Then, we discuss the amount of privacy budget that can be achieved with shot noise, which serves as a metric for the level of privacy protection. Furthermore, we provide the QDP of shot noise in quantum circuits with depolarizing noise. Through numerical simulations, we show that shot noise can effectively provide privacy protection in quantum computing.

DOI
10.1109/qcnc62729.2024.00045

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Quantum computing has been widely applied in various fields, such as quantum physics simulations, quantum machine learning, and big data analysis. However, in the domains of data-driven paradigms, ensuring databases privacy. For classical computing, we can incorporate the concept of differential privacy (DP) to meet the standard of privacy preservation by manually adding noise. In quantum computing scenario, researchers have extended classic DP to quantum differential privacy (QDP) by considering quantum noise. In this paper, we propose a novel approach to satisfy the QDP definition by considering the errors generated by the projection operator measurement, which is denoted as shot noise. Then, we discuss the amount of privacy budget that can be achieved with shot noise, which serves as a metric for the level of privacy protection. Furthermore, we provide the QDP of shot noise in quantum circuits with depolarizing noise. Through numerical simulations, we show that shot noise can effectively provide privacy protection in quantum computing.
While quantum computing has strong potential in data-driven fields, the privacy issue of sensitive or valuable information involved in the quantum algorithm should be considered. Differential privacy (DP), which is a fundamental privacy tool widely used in the classical scenario, has been extended to the quantum domain, i.e., quantum differential privacy (QDP). QDP may become one of the most promising approaches toward privacy-preserving quantum computing since it is not only compatible with classical DP mechanisms but also achieves privacy protection by exploiting unavoidable quantum noise in noisy intermediate-scale quantum (NISQ) devices. This paper provides an overview of the various implementations of QDP and their performance in terms of privacy parameters under the DP setting. Specifically, we propose a taxonomy of QDP techniques, categorizing the literature on whether internal or external randomization is used as a source to achieve QDP and how these implementations are applied to each phase of the quantum algorithm. We also discuss challenges and future directions for QDP. By summarizing recent advancements, we hope to provide a comprehensive, up-to-date review for researchers venturing into this field.
Quantum computing has revolutionized the approach to solving complex problems and handling vast datasets. However, data leakage in quantum computing may present privacy risks. While differential privacy (DP) has been a classical solution to protect privacy by injecting artificial noises, the implementation of DP within the quantum domain remains an under-explored area. We observe that there is a potential to leverage the inherent noises generated by Noisy Intermediate-Scale Quantum (NISQ) devices during quantum operations for achieving DP in quantum computing. Given that these inherent noises are uncontrollable, we take the lead to utilize quantum error mitigation (QEM) techniques to manage quantum differential privacy (QDP) protection levels. In our approach, we offer a tangible description of the "closeness" between neighboring quantum datasets and introduce a novel QDP definition based on observables of interest. Our simulations reveal that factors like the distance between neighboring quantum states and the number of circuit executions influence the privacy budget. Notably, by controlling the QEM samples while keeping the number of circuit executions fixed, QEM allows for substantial noise control to meet a desired privacy budget. Furthermore, we extend analysis from single-qubit to multiple qubits and discuss the impact of observables of interest on the privacy budget.
Quantum computing is a promising paradigm for efficiently solving large and high-complexity problems. However, ensuring privacy within this quantum computing necessitates innovative approaches. Existing research has introduced the concept of quantum differential privacy (QDP) to protect data privacy in quantum computing by leveraging quantum noise. Yet, this method faces limitations due to the fixed and uncontrollable nature of the inherent noise, which directly affects the privacy budget of QDP. Addressing this critical gap, our study proposes a novel approach that utilizes quantum error correction (QEC) techniques not only to mitigate quantum computing errors but also to adjust QDP protection levels precisely. By selectively applying QEC to single or multiple qubit gates, we introduce a method to manipulate the quantum noise error rate effectively. Moreover, we derive a new formula for calculating the overall error rate in a quantum circuit and the adjusted privacy budget after QEC operation. Through extensive numerical simulations, we validate the efficacy of utilizing QEC in tuning privacy protection levels within quantum computing.
Quantum computing revolutionizes the way of solving complex problems and handling vast datasets, which shows great potential to accelerate the machine learning process. However, data leakage in quantum machine learning (QML) may present privacy risks. Although differential privacy (DP), which protects privacy through the injection of artificial noise, is a well-established approach, its application in the QML domain remains under-explored. In this paper, we propose to harness inherent quantum noises to protect data privacy in QML. Especially, considering the Noisy Intermediate-Scale Quantum (NISQ) devices, we leverage the unavoidable shot noise and incoherent noise in quantum computing to preserve the privacy of QML models for binary classification. We mathematically analyze that the gradient of quantum circuit parameters in QML satisfies a Gaussian distribution, and derive the upper and lower bounds on its variance, which can potentially provide the DP guarantee. Through simulations, we show that a target privacy protection level can be achieved by running the quantum circuit a different number of times.
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 Rényi differential privacy (QRDP), which incorporates the advantages of classical Rényi DP and is applicable in the QDC domain. Based on the new quantum Rényi divergence, QRDP provides delicate and flexible privacy protection by introducing parameter ααα. 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.
Quantum computing offers unparalleled processing power but raises significant data privacy challenges.Quantum Differential Privacy(QDP) leverages inherent quantum noise to safeguard privacy, surpassing traditional DP. This paper develops comprehensive noise profiles, identifies noise types beneficial for QDP, and highlights the need for practical implementations beyond theoretical models. Existing QDP mechanisms, limited to single noise sources, fail to reflect the multi-source noise reality of quantum systems. We propose a resilient hybrid QDP mechanism utilizing channel and measurement noise, optimizing privacy budgets to balance privacy and utility. Additionally, we introduceLifted Quantum Differential Privacy, offering enhanced randomness for improved privacy audits and quantum algorithm evaluation.