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 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.
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 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 α. 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.
This paper provides an integrated perspective on addressing key challenges in developing reliable and secure Quantum Neural Networks (QNNs) in the Noisy Intermediate-Scale Quantum (NISQ) era. In this paper, we present an integrated framework that leverages and combines existing approaches to enhance QNN efficiency, security, and privacy. Specifically, established optimization strategies, including efficient parameter initialization, residual quantum circuit connections, and systematic quantum architecture exploration, are integrated to mitigate issues such as barren plateaus and error propagation. Moreover, the methodology incorporates current defensive mechanisms against adversarial attacks. Finally, Quantum Federated Learning (QFL) is adopted within this framework to facilitate privacy-preserving collaborative training across distributed quantum systems. Collectively, this synthesized approach seeks to enhance the robustness and real-world applicability of QNNs, laying the foundation for reliable quantum-enhanced machine learning applications in finance, healthcare, and cybersecurity.
This paper explores a novel approach to achieving differential privacy in quantum computations through entangled data sharing. Traditional differential privacy methods struggle to scale effectively in complex quantum algorithms, largely due to the inherent sensitivity of quantum states. We propose encoding sensitive data into entangled quantum states and performing computations on these states. The utilization of entanglement fundamentally alters the privacy landscape, offering the potential for enhanced privacy guarantees compared to classical differential privacy techniques. The core idea is that the correlations introduced by entanglement will make it significantly harder to infer information about individual data points from the resulting quantum state, even with access to a significant amount of quantum computation. We provide a theoretical framework outlining this approach and discuss the potential implications for secure quantum data analysis. The analysis focuses on the impact of entanglement on the privacy loss parameter, δ, demonstrating a possible reduction in privacy loss compared to standard methods. This work represents a crucial step towards realizing the full potential of quantum computing while simultaneously maintaining strong privacy protections.