Differentially private quantum sensor networks

2026-07-07

AI Summary

This work addresses the vulnerability of quantum sensing networks to privacy attacks when handling sensitive data, a challenge exacerbated by the difficulty of existing entanglement-based protocols in simultaneously achieving high precision and strong privacy guarantees. The study introduces differential privacy into quantum sensing for the first time, proposing a novel protocol that injects noise directly into the sensing Hamiltonian. Under the assumption of honest nodes, this approach achieves $(\varepsilon, \delta)$-differential privacy with arbitrarily small $\delta$, while preserving Heisenberg-limited mean squared error scaling ($O(1/n^2)$). By integrating entangled sensing, distributed randomness, and locally implementable mechanisms, the protocol effectively resists both classical and quantum adversaries and demonstrates a superior privacy–utility trade-off compared to classical schemes, thereby establishing a clear quantum advantage.

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.

Problem

• differential privacy
• quantum sensor networks
• privacy-preserving sensing
• Heisenberg-limited scaling
• function estimation

Innovation

• differential privacy
• quantum sensor networks
• Heisenberg scaling
• entanglement
• privacy-preserving sensing