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arXiv 2102.01615cs.NI
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用于对等网络的统计隐私保护消息分发

Statistical privacy-preserving message dissemination for peer-to-peer networks

David Mödinger, Jan-Hendrik Lorenz, Fanz J. Hauck

摘要:

对通信隐私的关注在研究和整个社会中得到广泛讨论。对于区块链的公共金融基础设施,这种讨论包括交易数据的隐私及其在整个网络中的广播。为了解决这个问题,我们将无限树上的接触网络的离散时间协议转化为对等网络的计算机网络协议。对等网络被建模为有机增长图。我们发现,在这样的网络中的最短路径的分布可以使用正态分布$\mathcal{N}(μ,σ^2).$建模。我们通过多变量模型确定$μ,σ$的统计估计。该模型的行为对数的节点数n和成比例的增加的边缘数k的逆指数。这些结果有利于在有限的信息环境中的最佳隐私传播阶段的最佳转发概率的计算。

英文摘要:

Concerns for the privacy of communication is widely discussed in research and overall society. For the public financial infrastructure of blockchains, this discussion encompasses the privacy of transaction data and its broadcasting throughout the network. To tackle this problem, we transform a discrete-time protocol for contact networks over infinite trees into a computer network protocol for peer-to-peer networks. Peer-to-peer networks are modeled as organically growing graphs. We show that the distribution of shortest paths in such a network can be modeled using a normal distribution $\mathcal{N}(μ,σ^2).$ We determine statistical estimators for $μ,σ$ via multivariate models. The model behaves logarithmic over the number of nodes n and proportional to an inverse exponential over the number of added edges k. These results facilitate the computation of optimal forwarding probabilities during the dissemination phase for optimal privacy in a limited information environment.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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