提出隐私保护的分布式证据融合算法，防止偏好泄露且结果接近中心化方法。
A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making
- 通过不可逆变换保护证据隐私，避免直接交换原始数据。
- 三阶段共识机制实现证据一致性，收敛性可证明且无法反推原始证据。
- 适合需要隐私保护的集体决策场景，如多智能体协同或联邦学习。
近年来，证据推理理论被用于集体决策。然而，现有分布式证据融合方法因直接交换原始证据，导致参与者偏好泄露和融合失败，未能像中心化可信证据融合（CCEF）那样评估证据可信度。本文提出一种三级共识的隐私保护分布式可信证据融合方法（PCEF）。在邻域证据差异度量（EDM）共识中，通过共享点积协议与最大主观概率事件的一致判断，推导出相邻代理间无证据等价表达式，实现不可逆的证据转换以保障隐私。在网络层EDM共识中，利用线性平均共识（LAC）与自适应秩低秩矩阵补全，推断非邻接EDM并使邻接EDM趋于一致，保证共识收敛且不以数值迭代方式求解原始证据。在融合网络共识中，提出带自抵消差分隐私项的隐私保护LAC，各代理在分享内容中添加随机噪声，并在共识迭代中逐步消除。进一步分析了收敛至CCEF的充分条件，证明在迭代过程中原始证据无法被反推。仿真表明，PCEF在可信度和融合结果上接近CCEF，且决策准确率更高、耗时更少。
原文摘要 · Abstract (English)
The theory of evidence reasoning has been applied to collective decision-making in recent years. However, existing distributed evidence fusion methods lead to participants' preference leakage and fusion failures as they directly exchange raw evidence and do not assess evidence credibility like centralized credible evidence fusion (CCEF) does. To do so, a privacy-preserving distributed credible evidence fusion method with three-level consensus (PCEF) is proposed in this paper. In evidence difference measure (EDM) neighbor consensus, an evidence-free equivalent expression of EDM among neighbored agents is derived with the shared dot product protocol for pignistic probability and the identical judgment of two events with maximal subjective probabilities, so that evidence privacy is guaranteed due to such irreversible evidence transformation. In EDM network consensus, the non-neighbored EDMs are inferred and neighbored EDMs reach uniformity via interaction between linear average consensus (LAC) and low-rank matrix completion with rank adaptation to guarantee EDM consensus convergence and no solution of inferring raw evidence in numerical iteration style. In fusion network consensus, a privacy-preserving LAC with a self-cancelling differential privacy term is proposed, where each agent adds its randomness to the sharing content and step-by-step cancels such randomness in consensus iterations. Besides, the sufficient condition of the convergence to the CCEF is explored, and it is proven that raw evidence is impossibly inferred in such an iterative consensus. The simulations show that PCEF is close to CCEF both in credibility and fusion results and obtains higher decision accuracy with less time-comsuming than existing methods.
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