arXiv subjects

Chaoxiong Ma

Publications and source records attributed to Chaoxiong Ma.

3 recordsLinked to original sources

arXiv2025

Decision-oriented joint optimization of evidence fusion based on event-conditioned credibility

In decision-level fusion tasks involving heterogeneous sources with unequal precision and potential anomalies, evidence deviating from the majority may be either critical evidence supporting the correct decision or anomalous evidence supporting an incorrect event. Existing credible evidence fusion methods primarily assess credibility through inter-evidence comparisons and may consequently underestimate critical evidence. This paper proposes event-conditioned credibility to characterize the relative credibility of evidence under different candidate-event hypotheses. A decision-oriented joint optimization model then couples credibility calculation, evidence fusion, and event decision through candidate-event probabilities. The model is expressed as a continuous self-mapping on the probability simplex. A direct fixed-point iteration provides the default fast solver, while a Kuhn simplicial search supplies a mesh-dependent approximate fixed point if the direct iteration forms a periodic orbit. A plausibility--belief arithmetic--geometric divergence is further proposed to calculate event-conditioned credibility. Numerical experiments show that the proposed method gives credibility rankings that better reflect the contribution of evidence to the ground truth and provides greater support for the correct event than representative credible evidence fusion methods. Monte Carlo tests additionally demonstrate a high empirical fixed-point attainment rate over the tested settings.

cs.AI↗

arXiv2024

Credible fusion of evidence in distributed system subject to cyberattacks

Given that distributed systems face adversarial behaviors such as eavesdropping and cyberattacks, how to ensure the evidence fusion result is credible becomes a must-be-addressed topic. Different from traditional research that assumes nodes are cooperative, we focus on three requirements for evidence fusion, i.e., preserving evidence's privacy, identifying attackers and excluding their evidence, and dissipating high-conflicting among evidence caused by random noise and interference. To this end, this paper proposes an algorithm for credible evidence fusion against cyberattacks. Firstly, the fusion strategy is constructed based on conditionalized credibility to avoid counterintuitive fusion results caused by high-conflicting. Under this strategy, distributed evidence fusion is transformed into the average consensus problem for the weighted average value by conditional credibility of multi-source evidence (WAVCCME), which implies a more concise consensus process and lower computational complexity than existing algorithms. Secondly, a state decomposition and reconstruction strategy with weight encryption is designed, and its effectiveness for privacy-preserving under directed graphs is guaranteed: decomposing states into different random sub-states for different neighbors to defend against internal eavesdroppers, and encrypting the sub-states' weight in the reconstruction to guard against out-of-system eavesdroppers. Finally, the identities and types of attackers are identified by inter-neighbor broadcasting and comparison of nodes' states, and the proposed update rule with state corrections is used to achieve the consensus of the WAVCCME. The states of normal nodes are shown to converge to their WAVCCME, while the attacker's evidence is excluded from the fusion, as verified by the simulation on a distributed unmanned reconnaissance swarm.

cs.CR↗

arXiv2024

A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making

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.

cs.AI↗