论文标题
保留Friedkin-Johnsen系统中影响结构的隐私
Preserving Privacy of the Influence Structure in Friedkin-Johnsen Systems
论文作者
论文摘要
在共同分布式共识算法中共享的信息共享的性质允许网络窃听者揭示敏感的系统信息。分布式系统中通常在隐私保护范围下忽略的一个重要参数是影响结构 - 每个代理在其意见池的来源上加权。本文提出了一个本地（即每个代理人单独计算的），时间改变掩码，以防止外部观察者发现影响结构，并访问整个信息流，网络知识和掩盖公式。该结果是通过在一组广义条件下的弗里德金 - 约翰逊系统保留的稳定性的辅助演示产生的。掩模是在这些约束下开发的，涉及通过腐烂的伪造来扰动影响结构。本文提供了缺乏先验知识的窃听器的最佳影响力结构估算的信息矩阵，并使用随机模拟来分析面具的性能针对范围的系统超参数。
The nature of information sharing in common distributed consensus algorithms permits network eavesdroppers to expose sensitive system information. An important parameter within distributed systems, often neglected under the scope of privacy preservation, is the influence structure - the weighting each agent places on the sources of their opinion pool. This paper proposes a local (i.e. computed individually by each agent), time varying mask to prevent the discovery of the influence structure by an external observer with access to the entire information flow, network knowledge and mask formulation. This result is produced through the auxiliary demonstration of the preserved stability of a Friedkin-Johnsen system under a set of generalised conditions. The mask is developed under these constraints and involves perturbing the influence structure by decaying pseudonoise. This paper provides the information matrix of the best influence structure estimate by an eavesdropper lacking a priori knowledge and uses stochastic simulations to analyse the performance of the mask against ranging system hyperparameters.
