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arXiv:2403.09173 · IEEE International Conference on Quantum Computing and Engineering (QCE)

Bridging Quantum Computing and Differential Privacy: Insights into Quantum Computing Privacy

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

Published Sep 15, 2024• 5 citations
arXivHTMLPDF

Abstract

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.

DOI
10.1109/qce60285.2024.00012

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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 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.
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 networks show promise in enhancing the overall functional advantages of the Internet and enabling applications that are incomparable in the classical world. Supported by quantum communication, quantum networks are expected to offer almost unconditional security. However, securing personal privacy within quantum networks is of paramount importance. The aim of this paper is to extend the differential privacy (DP) framework to quantum networks, addressing the issue of personal privacy security in the future quantum networks. We begin by discussing the fundamental principles of quantum computing and DP. Additionally, in the current noisy intermediate-scale quantum (NISQ) era, unavoidable noise is inherent in quantum computing. We explore in detail how this inherent noise can be utilized to protect personal privacy while also enhancing the utility of data. Finally, we analyze several pressing challenges and open research directions in personal privacy protection within future quantum networks.
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.
The exponential expansion of data-driven technologies underscores the critical need for robust privacy safeguards. This study introduces an innovative privacy-preserving framework that combines Differential Privacy (DP) and Federated Learning (FL) with the cutting-edge capability of quantum computing. Leveraging Quantum Random Number Generators (QRNGs), the framework incorporates truly random quantum-generated noise into the differential privacy mechanism, substantially enhancing the security of gradient updates within machine learning models. This noise is specifically added to the gradient updates during the optimization process, using various optimizers. Quantum mechanics' inherent randomness ensures unpredictability, fortifying defenses against sophisticated attacks. This research signifies a significant step towards the development of secure and ethical AI technologies. By integrating quantum computing's random number generation capabilities into privacy-preserving mechanisms, the research paves the way for enhanced data security across various sectors. The findings indicate a promising path for further investigation and experimentation, with potential applications in fields demanding high-stakes data protection. This fusion of quantum computing and privacy-preserving techniques not only enhances the confidentiality of sensitive information but also sets a new standard for privacy in the age of data-driven innovation.