基于车雾环境的联邦学习动态隐私保护方案

当前已经有研究将雾环境与联邦学习结合应用在车联网隐私保护中,但是缺乏对车辆移动性可能导致隐私需求改变的问题的考虑。为此,文中基于区域内车辆终端数目,提出了在不同的隐私需求下实施不同的隐私保护和效率调整的方案,在同态加密方案中进行双重加密聚合并且动态调整本地迭代次数,在差分隐私方案中动态调整每轮云聚合与雾聚合次数。实验表明,在区域内车辆终端数不同的情况下,本方案满足在隐私计算的同时保持较高精度。     

一类无向Kautz图的k限制边连通度的上界

在Moor-Shannon网络模型中,k限制边连通度较大的网络一般有较好的可靠性和容错性.本文在无向Kautz图UK(2,n)中研究k限制边连通度的上界ξk,证明了ξ5(UK(2,3))=6,ξ5(UK(2,n))=8,n≥4,且当4≤k≤n时,ξk(UK(2,n))≤2(k-「k/3」).     

一个新的隐私保护数据发布的匿名模型(英文)

Privacy-preserving data publishing(PPDP) is one of the hot issues in the field of the network security.The existing PPDP technique cannot deal with generality attacks,which explicitly contain the sensitivity attack and the similarity attack.This paper proposes a novel model,(w,y,k)-anonymity,to avoid generality attacks on both cases of numeric and categorical attributes.We show that the optimal(w,y,k)-anonymity problem is NP-hard and conduct the Top-down Local recoding(TDL) algorithm to implement the model.Our experiments validate the improvement of our model with real data.