一种用于集体决策的隐私保护分布式可信证据融合算法
A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making
AI总结：
针对分布式证据融合中的隐私泄露与可信度评估缺失问题，提出三级共识PCEF，在保护原始证据隐私的同时逼近CCEF，并提升决策准确率、降低耗时。
摘要：
近年来，证据推理理论已被应用于集体决策。然而，现有的分布式证据融合方法由于直接交换原始证据，且未能像集中式可信证据融合（CCEF）那样评估证据可信度，会导致参与者偏好泄露和融合失败。为此，本文提出了一种具有三级共识的隐私保护分布式可信证据融合方法（PCEF）。在证据差异度量（EDM）邻居共识中，利用pignistic概率的共享点积协议以及对两个具有最大主观概率事件的相同判断，推导了相邻智能体之间EDM的无证据等价表达，从而凭借这种不可逆的证据转换保证证据隐私。在EDM网络共识中，通过线性平均共识（LAC）与带秩自适应的低秩矩阵补全之间的交互，推断非相邻EDM并使相邻EDM达到一致，以保证EDM共识收敛，并且在数值迭代形式下不存在推断原始证据的解。在融合网络共识中，提出了一种带自抵消差分隐私项的隐私保护LAC，其中每个智能体将其随机性加入共享内容，并在共识迭代中逐步抵消该随机性。此外，探讨了收敛至CCEF的充分条件，并证明了在这种迭代共识中不可能推断出原始证据。仿真表明，PCEF在可信度和融合结果上均接近CCEF，并且相比现有方法以更少耗时获得更高的决策准确率。
英文摘要：
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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