distributed dp sensing

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.

This work addresses the fundamental challenge of achieving efficient and highly accurate estimation of the sum of user data under local differential privacy (LDP) in the honest-but-curious server model. The authors propose a novel correlated noise mechanism that, within a pure LDP framework, injects carefully designed correlated noise at the user side and leverages a distributed summation protocol to enable privacy-preserving computation. This approach is the first to demonstrate that LDP with correlated noise can attain estimation error arbitrarily close to the theoretical lower bound achievable in the centralized differential privacy setting. By doing so, it overcomes the well-known utility limitations of traditional LDP mechanisms that rely on independent noise, achieving near-optimal accuracy with only an arbitrarily small, tunable gap from the centralized lower bound.

In distributed client-server-verifier architectures, malicious servers may tamper with noise distributions—e.g., via sampling bias or artificial correlations—thereby breaking differential privacy (DP) guarantees. To address this, we propose Verifiable Distributed Differential Privacy (VDDP), the first formal framework for verifying DP compliance in such settings. We establish a rigorous definition of VDDP and reveal a sufficient (but not necessary) connection between zero-knowledge proofs (ZKPs) and DP verifiability. We design two efficient mechanisms: (i) the Verifiable Discrete Laplace Mechanism (VDDLM), which accelerates proof generation by 4×10⁵× while incurring only 0.1–0.2× the error of baseline mechanisms; and (ii) Verifiable Randomized Response (VRR), which reduces both communication and verification overhead by 5000×. Collectively, VDDP significantly enhances the practicality and trustworthiness of deploying DP in distributed systems.

In distributed multi-party secure computation, achieving both security and efficiency becomes challenging when the number of adversaries exceeds half the total participants (t ≥ n/2). Method: This paper proposes a differentially private secure multiplication protocol over the real numbers. Its core innovation is a novel “noise-layered encoding” mechanism, where multiple layers of controlled random noise are superimposed to ensure adversarial indistinguishability, while honest parties can decode layer-by-layer to recover exact results. Contributions/Results: First, it formally models the accuracy–privacy trade-off inherent in differential privacy. Second, it proves that only t+1 parties suffice to tolerate t colluding adversaries—breaking the classical information-theoretic requirement of 2t+1 parties (e.g., BGW). Third, it provides rigorous ε-differential privacy guarantees with bounded accuracy loss, while significantly reducing communication and computational overhead compared to prior approaches.

This work addresses graph data analysis under Local Differential Privacy (LDP), focusing on two fundamental tasks: *k*-core decomposition and triangle counting. Existing LDP methods suffer from high error—scaling with the total number of edges—leading to poor accuracy. To overcome this, we propose the first modeling framework based on *private out-degree orientation*, shifting the dominant term in the error bound from total edge count to the graph’s degeneracy—a typically much smaller parameter. Our approach integrates an improved randomized response mechanism, input-dependent noise injection, and private estimation of structural graph properties, all while requiring no trusted third party. Experiments demonstrate substantial gains: for *k*-core decomposition, absolute error is only 3× the exact value (versus 131× for baselines); for triangle counting, multiplicative error drops by six orders of magnitude. Moreover, our method maintains high computational efficiency.

This work investigates the design of sparse discrete perturbation mechanisms under local differential privacy, aiming to simultaneously achieve strong privacy guarantees and output sparsity. The authors propose an input-dependent sparse private channel that employs truncated discrete Laplace and Gaussian kernels, balancing privacy and sparsity by controlling the size of the output support set. They provide the first precise characterization of necessary and sufficient conditions for such mechanisms to satisfy pure and approximate local differential privacy, revealing the support size as the key parameter governing mechanism complexity. Furthermore, they derive an explicit privacy–sparsity trade-off: nontrivial approximate privacy necessitates a minimal support size, and for Gaussian mechanisms, the privacy loss grows quadratically with the support radius.

This study addresses the challenge of preserving topological privacy in consensus networks by preventing accurate reconstruction of the true network topology from observational data, while maintaining consensus behavior. By introducing a feedback mechanism that disrupts the conditions required for topological identifiability, the work establishes, for the first time, a theory of topological non-identifiability under both partial and full observation settings. It further proposes a distributed topology perturbation scheme that adheres to a prescribed privacy budget, enabling a controllable trade-off between consensus accuracy and privacy protection under local communication constraints. Integrating feedback control, distributed algorithm design, and low-complexity optimization, the proposed method significantly enhances edge-level privacy in simulations, outperforming existing approaches while retaining strong convergence performance.

本文提出CRSF协议，通过PBFT和阈值保护等方法解决隐私保护传感器融合中的共谋及拜占庭问题，保证安全性和性能。

This work addresses the challenge of precisely characterizing the overall privacy guarantee when composing mechanisms under multiple heterogeneous differential privacy (DP) constraints. The authors propose a general composition framework that, for the first time, enables an exact description of the resulting privacy region after composing an arbitrary number of mechanisms subject to diverse DP bounds. By constructing a binary hypothesis testing–based mixture model and integrating probabilistic mixing with f-DP approximation techniques, the framework yields an exact composition theorem for multiple DP constraints. Moreover, the approach naturally extends to the f-DP setting, significantly enhancing both the tightness and applicability of compositional privacy analysis.

本文针对多方差分隐私中连续噪声采样存在的安全漏洞，提出了一种基于离散采样的改进方法，提高了安全性和效率。