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Lecture notes in computer science

Enhancing Privacy with Quantum Networks

Paulo Mateus, Nikola Paunković, João Rodrigues, André Souto

Published Jan 1, 2014• 1 citation
Publisher
DOI
10.1007/978-3-662-44885-4_14

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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 hold the promise of enabling secure and trustworthy communication and computation. However, they also pose significant challenges in terms of security, privacy, and trust. In this paper, we have explored the current state-of-the-art in the field of quantum network security, privacy, and trust, with a focus on quantum key distribution, quantum-resistant cryptography, quantum hacking, trust establishment, and privacy-enhancing technologies. We have identified key challenges and open research questions in these areas, and presented potential solutions to enable the realization of secure and trustworthy quantum networks. As quantum networks continue to evolve, addressing these challenges will be crucial to realizing their full potential for secure communication and computation.
Private distributed quantum sensing aims to estimate an authorized collective parameter while preventing independent estimation of individual local parameters. Here, we analytically characterize Gaussian quantum networks satisfying this perfect local privacy condition. Using a graph representation of the Gaussian pairing matrix, we show that two-mode squeezed vacuum states constitute the essential building blocks of privacy-preserving Gaussian states. We then analytically derive the optimal sensitivity under perfect local privacy and determine a Gaussian probe that achieves Heisenberg scaling with respect to both the photon number and the number of sensing modes. Using local homodyne measurements and maximum-likelihood estimation, we numerically verify quantum-enhanced sensitivity beyond the shot-noise limit while preserving perfect local privacy. We further show that the local-privacy condition is preserved under arbitrary phase-independent quantum channels, including optical loss. Our results provide a general framework for constructing and optimizing privacy-preserving continuous-variable quantum sensing networks.
The rapid proliferation of wireless body area networks (WBANs) has intensified the need for architectures that simultaneously guarantee quantum-resistant security, patient privacy, and clinical-grade diagnostic fidelity. This paper introduces quantum-enhanced privacy aggregation (QEPA), a framework that establishes a new state-of-the-art in secure IoMT by fulfilling the “Triple Constraint” of Privacy, Robustness, and Diagnostic Fidelity, albeit with a critical dependency on line-of-sight quantum channels. QEPA’s defense-in-depth architecture integrates four synergistic innovations: (i) BB84 Quantum Key Distribution providing information-theoretic security ([Formula: see text]), though constrained to static or low-mobility scenarios due to free-space optical requirements; (ii) lightweight homomorphic encryption (LightHE) over [Formula: see text] enabling exact integer arithmetic with [Formula: see text] complexity; (iii) Hierarchical Federated Learning with Krum-based Byzantine resilience against [Formula: see text] compromised nodes; and (iv) SHAP-based explainable AI achieving 97.8% attribution fidelity against cardiologist-validated ground truth. Evaluated on a large-scale WBAN with [Formula: see text] sensors across 200 virtual patients, QEPA demonstrates near-optimal utility, achieving 96.8% diagnostic accuracy (only 0.3% below FedAvg) with NRMSE = [Formula: see text], while providing unprecedented efficiency: 8.05× lower latency (31.8 ms) and 5.96× lower energy (3.32 mJ) versus Paillier, alongside 17.1× communication compression (7.68 kbit). Critically, QEPA reduces membership inference risk to 1.5% (vs. 28.3% in FedAvg) while maintaining [Formula: see text] differential privacy and resisting quantum attacks with [Formula: see text]-operation hardness. While the current implementation’s reliance on line-of-sight QKD limits deployment in high-mobility clinical environments, these results establish QEPA as quantum-safe framework suitable for FDA-cleared life-critical medical telemetry in controlled settings, with clear pathways toward non-line-of-sight quantum networking.