Differentially private quantum sensor networks
- URL: http://arxiv.org/abs/2607.06521v1
- Date: Tue, 07 Jul 2026 17:27:30 GMT
- Title: Differentially private quantum sensor networks
- Abstract summary: We show that entangled quantum sensor networks are vulnerable to certain privacy-violating attacks.<n>We introduce secure sensing protocols endowed with differential privacy.<n>We prove that our protocols are resilient to attacks by broad classes of classical and quantum adversaries.
- Score: 1.5006258585503878
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Experimental Private Quantum Networked Sensing [0.7869752673792281]
We introduce a noise-robust protocol for private quantum networked sensing.<n>We simulate attacks using three different quantum states, a four-qubit GHZ state, two Bell pairs, and a fully separable state.
arXiv Detail & Related papers (2026-09-15T07:16:53Z) - Certification of Network Quantum Sensing [0.6041648831662462]
We introduce a quantum remote sensing protocol that can rigorously certify privacy and integrity of the estimation.<n>We experimentally demonstrate this framework by estimating an optical phase using entangled photons.
arXiv Detail & Related papers (2026-06-09T11:02:50Z) - Universal Operational Privacy in Distributed Quantum Sensing [3.758871793953489]
We introduce a universal operational privacy framework for distributed quantum sensing networks.<n>A protocol-independent criterion ensures that no information about individual parameters is accessible to untrusted parties.<n>Our results establish universal operational constraints governing privacy in distributed quantum sensing networks.
arXiv Detail & Related papers (2026-01-27T05:17:46Z) - Anonymous and private parameter estimation in networks of quantum sensors [1.099532646524593]
In quantum networks, distributed quantum sensing has emerged as a powerful use case.<n>We develop a new protocol combining the different cryptographic functionalities of anonymity and privacy for the task of distributed parameter estimation.
arXiv Detail & Related papers (2025-07-01T18:02:51Z) - Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning [54.20871516148981]
We introduce the Communication-Efficient and Privacy-Adaptable Mechanism (CEPAM)<n>CEPAM achieves communication efficiency and privacy protection simultaneously.<n>We theoretically analyze the privacy guarantee of CEPAM and investigate the trade-offs among user privacy and accuracy of CEPAM.
arXiv Detail & Related papers (2025-01-21T11:16:05Z) - Quantum-private distributed sensing [35.42935514723487]
Quantum networks can enhance both security and privacy conditions for multi-user communication, delegated computation, and distributed sensing tasks.<n>An example quantum protocol is private parameter estimation (PPE) where only the aggregate information is accessible while individual sensor data remain confidential.<n>We implement the PPE protocol by distributing a three-photon Greenberger-Horne-Zeilinger (GHZ) state, among three sensors, which is verified using stabilizer measurements to establish privacy and precision bounds for the sensing task.
arXiv Detail & Related papers (2024-10-01T18:00:08Z) - Utilizing probabilistic entanglement between sensors in quantum networks [8.59730790789283]
One of the most promising applications of quantum networks is entanglement assisted sensing.
This work outlines when and how to use entanglement, when to store it, and when it needs to be distilled.
arXiv Detail & Related papers (2024-07-22T14:12:30Z) - Privacy-Preserving Individual-Level COVID-19 Infection Prediction via
Federated Graph Learning [33.77030569632993]
We focus on developing a framework of privacy-preserving individual-level infection prediction based on federated learning (FL) and graph neural networks (GNN)
We propose Falcon, a Federated grAph Learning method for privacy-preserving individual-levelfetion predictiON.
Our methodology outperforms state-of-the-art algorithms and is able to protect user privacy against actual privacy attacks.
arXiv Detail & Related papers (2023-11-10T13:22:14Z) - Breaking the Communication-Privacy-Accuracy Tradeoff with
$f$-Differential Privacy [51.11280118806893]
We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability.
We study the local differential privacy guarantees of discrete-valued mechanisms with finite output space through the lens of $f$-differential privacy (DP)
More specifically, we advance the existing literature by deriving tight $f$-DP guarantees for a variety of discrete-valued mechanisms.
arXiv Detail & Related papers (2023-02-19T16:58:53Z) - Robustness Threats of Differential Privacy [70.818129585404]
We experimentally demonstrate that networks, trained with differential privacy, in some settings might be even more vulnerable in comparison to non-private versions.
We study how the main ingredients of differentially private neural networks training, such as gradient clipping and noise addition, affect the robustness of the model.
arXiv Detail & Related papers (2020-12-14T18:59:24Z) - CryptoSPN: Privacy-preserving Sum-Product Network Inference [84.88362774693914]
We present a framework for privacy-preserving inference of sum-product networks (SPNs)
CryptoSPN achieves highly efficient and accurate inference in the order of seconds for medium-sized SPNs.
arXiv Detail & Related papers (2020-02-03T14:49:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.