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arXiv:2607.06521

Differentially private quantum sensor networks

Daniel J. Spencer, Kaiyan Shi, Emil T. Khabiboulline, Gorjan Alagic, Alexey V. Gorshkov

quant-phcs.CRcs.ITSubmitted Jul 7, 2026• Announced Jul 8, 2026• 0 citations
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Abstract

Quantum sensing is a promising technology capable of demonstrating clear advantage over comparable classical techniques for precise measurement. One application of quantum sensing is in function estimation, which can be done using a network of entangled quantum sensors, allowing for measurements with greater optimal sensitivity than unentangled sensing protocols. In cases where quantum sensor networks will be used to measure data that should remain private (e.g., biomedical data), it is imperative that these protocols include a privacy mechanism to hide sensitive information. In this work, we show that entangled sensor networks are vulnerable to certain privacy-violating attacks. To mitigate these attacks, we introduce secure sensing protocols endowed with differential privacy. We reconcile differential privacy with retaining Heisenberg-limited scaling, and introduce several protocols achieving varying balances between the two. We show that our main protocol, an $n$-node network sensing protocol that injects noise directly into the sensing Hamiltonian, exhibits a tradeoff between the desirable $O(1/n^2)$ Heisenberg scaling of the mean-squared error of the function estimate and the level of privacy attainable. Under assumptions on the network (a common source of randomness and a constant fraction of honest parties), we show that this protocol is locally implementable and achieves $(O(1), δ)$-differential privacy for arbitrarily small $δ$ while retaining Heisenberg scaling of the mean-squared error. We prove that our protocols are resilient to attacks by broad classes of classical and quantum adversaries, and find advantages in the privacy-utility tradeoff when using quantum techniques.

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36 pages, 5 figures, 1 table

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Quantum sensing is a promising technology capable of demonstrating clear advantage over comparable classical techniques for precise measurement. One application of quantum sensing is in function estimation, which can be done using a network of entangled quantum sensors, allowing for measurements with greater optimal sensitivity than unentangled sensing protocols. In cases where quantum sensor networks will be used to measure data that should remain private (e.g., biomedical data), it is imperative that these protocols include a privacy mechanism to hide sensitive information. In this work, we show that entangled sensor networks are vulnerable to certain privacy-violating attacks. To mitigate these attacks, we introduce secure sensing protocols endowed with differential privacy. We reconcile differential privacy with retaining Heisenberg-limited scaling, and introduce several protocols achieving varying balances between the two. We show that our main protocol, an nnn-node network sensing protocol that injects noise directly into the sensing Hamiltonian, exhibits a tradeoff between the desirable O(1/n2)O(1/n^2)O(1/n2) Heisenberg scaling of the mean-squared error of the function estimate and the level of privacy attainable. Under assumptions on the network (a common source of randomness and a constant fraction of honest parties), we show that this protocol is locally implementable and achieves (O(1),δ)(O(1), δ)(O(1),δ)-differential privacy for arbitrarily small δδδ while retaining Heisenberg scaling of the mean-squared error. We prove that our protocols are resilient to attacks by broad classes of classical and quantum adversaries, and find advantages in the privacy-utility tradeoff when using quantum techniques.
Entangling distant quantum sensors is a key application of future quantum networks, allowing for the estimation of global functions of local parameters, with precision that is not possible with stand-alone, individual, quantum sensors. However, with this advantage comes the risk of information leakage over the network via possible malicious parties. It is often particularly important that local parameters remain unknown and that only the global function is shared across the network. Recently, the notion of privacy has been introduced in this context, which ensures that only the agreed function of parameters is shared over the network, even when malicious parties control the network itself, whilst maintaining the estimation advantage. In this work, we introduce a noise-robust protocol for private quantum networked sensing, which we realise using a high-fidelity Greenberger-Horne-Zeilinger (GHZ) state source. We further run a comparative analysis and simulate attacks using three different quantum states, a four-qubit GHZ state, two Bell pairs, and a fully separable state. Our results highlight the clear advantage of GHZ states in maintaining high precision, accuracy, and privacy for the distributed estimation task.
Abstract Quantum networks can enhance both security and privacy conditions for multi-user communication, delegated computation, and distributed sensing tasks. In distributed quantum sensing, it is often desirable to extract only global information from a network of sensors while keeping individual sensor values hidden. Private parameter estimation (PPE), a recently proposed protocol, formalises this requirement by defining a notion of privacy that bounds information leakage about local parameters while allowing estimation of a global function. Here, we present a proof-of-principle implementation of PPE using a three-photon Greenberger–Horne–Zeilinger state distributed across three sensors and verified via stabiliser measurements. We demonstrate Heisenberg-limited scaling for the global parameter while suppressing local metrological information by up to three orders of magnitude, establishing both the privacy and precision performance of the protocol. This work, which integrates privacy in distributed quantum sensing, marks a crucial step towards developing advanced quantum-secure-and-private protocols in complex quantum networks.
Differential privacy provides a robust framework for protecting sensitive data, while maintaining its utility for computation. In essence, a differentially private algorithm takes as input the data of multiple parties, and returns an output disclosing minimal information about any individual party. Previous research has introduced several quantum extensions of differential privacy, with applications ranging from quantum machine learning on private classical data to quantum shadow tomography. However, the local model of quantum differential privacy – where each party is responsible for privatizing their own data at a local level – has received limited attention. This work delves into locally differentially private quantum measurements. Although any measurement can be made locally differentially private by adding noise to the outcome, we demonstrate that certain quantum measurements inherently satisfy some degree of local differential privacy for specific classes of input states. This finding has two significant implications: first, limiting the analysis to classical noise injection mechanisms may lead to suboptimal privacy-utility trade-offs for quantum data; second, the theory of differential privacy can be harnessed to further investigate the capabilities of quantum measurements. Motivated by these insights, we establish strong data processing inequalities for the quantum relative entropy under local differential privacy and apply these results to asymmetric hypothesis testing of quantum states with restricted measurements. Additionally, we prove an equivalence between quantum statistical queries and quantum differential privacy in the local model, thereby addressing an open question posed by Arunachalam et al. (2021). Finally, we consider the task of quantum multi-party computation under local differential privacy, demonstrating that parity functions can be efficiently learned in this model, whereas the corresponding classical task requires exponentially many samples.
Quantum sensor networks promise precision advantages over classical and single-sensor strategies, in particular when the estimator is non-local. We address the problem of finding such estimators through a framework we connote spatial quantum sensing: given an underlying field interrogated by a network of quantum sensors at fixed positions, construct an estimator for a property of the field, for example, distinguishing a source of signal, or evaluating the field or its derivatives at an arbitrary point. We first treat polynomial fields, casting the task as an interpolation problem, and then generalize to fields modeled by analytic functions, which yields general least-squares estimators. A central and largely unaddressed question is under what conditions on sensor placement these estimators are well-defined and error-free. For mmm-dimensional arrays we give explicit constructions and proofs in the interpolation setting using algebraic geometry, and establish necessary and sufficient conditions in the general case. Comparing a non-local entangled protocol with the best local strategy, we show that entanglement yields maximal precision in distributed sensing under global resource constraints. Finally, we introduce error-free subspaces, a technique that translates prior knowledge of the field into a reduction in the number of required sensors. We expect these techniques to be broadly useful in sensing problems across scales, ranging from earth-scale experiments to local applications such as biological imaging.
Quantum network sensing shows potential to enhance the estimation precision for functions of spatially distributed parameters beyond the shot noise limit. The key resource required for this task is possibly multi-partite quantum entanglement. Such protocols can also provide privacy, preventing sensitive information from leaking to unauthorized parties. The photonic entanglement is the most natural for this task; however, distributing it over long distances presents significant difficulties, mainly because of unavoidable loss in communication channels. The resource efficiency is also fundamental, both for precise network sensing and for private sensing In this research, we analyze a quantum network sensing protocol based on a recently proposed, efficient GHZ state distribution scheme. In comparison to conventional methods based on entanglement distribution, our protocol shows the decreasing loss-induced estimation error of certain functions of distributed parameters including their arbitrary linear combinations. Moreover, we consider a scenario in which one person can estimate linear combinations of distributed parameters without violating privacy of the other users. We show that an additional parameter can be used to hide the information about the target parameter from everyone, except the person who controls the parameter.
We introduce information-theoretically private quantum protocols for two-party Hamming distance when both parties must output the same estimate. Classically, for input length nnn, information-theoretic protocols require Ω(n)Ω(\sqrt{n})Ω(n​) error under pure differential privacy and Ω(n/log⁡n)Ω(\sqrt{n}/\log n)Ω(n​/logn) error under strong approximate differential privacy, whereas computational security permits O(1)O(1)O(1) error. In Klauck's honest, nonpreemptive, message-preserving model, we give an O(n)O(n)O(n)-communication quantum protocol with pure ε\varepsilonε quantum differential privacy (QDP) and expected error at most 2sinh⁡ε+γ\frac{2}{\sinh \varepsilon}+γsinhε2​+γ, for every γ>0γ>0γ>0. For approximate (ε,δ)(\varepsilon, δ)(ε,δ) QDP, an exact hockey-stick divergence calculation yields strictly smaller error, while preserving the O(1)O(1)O(1)-versus-Ω(n/log⁡n)Ω(\sqrt{n}/\log n)Ω(n​/logn) separation for δ=o(1/n)δ=o(1/n)δ=o(1/n). Thus, quantum communication achieves O(1)O(1)O(1) information-theoretic error, matching the accuracy available classically only under computational assumptions. The main construction uses a guarded coherent round trip and an equal-Gram rigidity principle that prevents an honest player from retaining input-dependent complementary information. We separate this model from weaker prescribed-channel privacy, which already admits an exact classical realization, and from fully retention-robust security, against which measurement-and-abort attacks remain possible. Therefore, we identify preservation of non-orthogonal quantum messages as a resource for privacy.