标题： 从孪生信任得分中重构信任嵌入：一种具有固定点语义的直接和方法 显示英文标题

标题： Reconstructing Trust Embeddings from Siamese Trust Scores: A Direct-Sum Approach with Fixed-Point Semantics

摘要： 我们研究从许多分布式安全框架所暴露的一维Siamese信任分数中重建高维信任嵌入的逆问题。 从两个独立代理开始，它们为同一组设备发布带时间戳的相似性分数，我们形式化了估计任务，推导出一个显式的直和估计器，该估计器将配对分数序列与四个矩特征连接起来，并证明在基于Banach理论的压缩论证下，所得的重构映射在收缩映射下具有唯一的不动点。 一套合成基准测试（20个设备×10个时间步）确认了即使在存在高斯噪声的情况下，恢复的嵌入通过欧几里得和余弦度量保持设备间的几何结构；我们通过非渐近误差界限补充这些实验，这些界限将重构精度与分数序列长度联系起来。 除了方法论之外，本文展示了实际的隐私风险：发布细粒度的信任分数可能会泄露关于设备和评估模型的潜在行为信息。 因此，我们讨论了对抗措施——分数量化、校准噪声、混淆嵌入空间——并将它们置于网络化AI系统中透明度与保密性的更广泛辩论之中。 所有数据集、再现脚本和扩展证明都随提交一起提供，以便在不使用专有代码的情况下验证结果。 显示英文摘要

摘要： We study the inverse problem of reconstructing high-dimensional trust embeddings from the one-dimensional Siamese trust scores that many distributed-security frameworks expose. Starting from two independent agents that publish time-stamped similarity scores for the same set of devices, we formalise the estimation task, derive an explicit direct-sum estimator that concatenates paired score series with four moment features, and prove that the resulting reconstruction map admits a unique fixed point under a contraction argument rooted in Banach theory. A suite of synthetic benchmarks (20 devices x 10 time steps) confirms that, even in the presence of Gaussian noise, the recovered embeddings preserve inter-device geometry as measured by Euclidean and cosine metrics; we complement these experiments with non-asymptotic error bounds that link reconstruction accuracy to score-sequence length. Beyond methodology, the paper demonstrates a practical privacy risk: publishing granular trust scores can leak latent behavioural information about both devices and evaluation models. We therefore discuss counter-measures -- score quantisation, calibrated noise, obfuscated embedding spaces -- and situate them within wider debates on transparency versus confidentiality in networked AI systems. All datasets, reproduction scripts and extended proofs accompany the submission so that results can be verified without proprietary code.