在定位请求服务中,如何保护用户的位置隐私和位置服务提供商(Localization service provider,LSP)的数据隐私是关系到WiFi指纹定位应用的一个具有挑战性的问题。基于密文域的K-近邻(K-nearest neighbors,KNN)检索,本文提出了一种适用于...在定位请求服务中,如何保护用户的位置隐私和位置服务提供商(Localization service provider,LSP)的数据隐私是关系到WiFi指纹定位应用的一个具有挑战性的问题。基于密文域的K-近邻(K-nearest neighbors,KNN)检索,本文提出了一种适用于三方的定位隐私保护算法,能有效提升对LSP指纹信息隐私的保护强度并降低计算开销。服务器和用户分别完成对指纹信息和定位请求的加密,而第三方则基于加密指纹库和加密定位请求,在隐私状态下完成对用户的位置估计。所提算法把各参考点的位置信息随机嵌入指纹,可避免恶意用户获取各参考点的具体位置;进一步利用布隆滤波器在隐藏接入点信息的情况下,第三方可完成参考点的在线匹配,实现对用户隐私状态下的粗定位,可与定位算法结合降低计算开销。在公共数据集和实验室数据集中,对两种算法的安全、开销和定位性能进行了全面的评估。与同类加密算法比较,在不降低定位精度的情况下,进一步增强了对数据隐私的保护。展开更多
Machine learning algorithms are considered as effective methods for improving the effectiveness of neutron-gamma(n-γ)discrimination.This study proposed an intelligent discrimination method that combined a Gaussian mi...Machine learning algorithms are considered as effective methods for improving the effectiveness of neutron-gamma(n-γ)discrimination.This study proposed an intelligent discrimination method that combined a Gaussian mixture model(GMM)with the K-nearest neighbor(KNN)algorithm,referred to as GMM-KNN.First,the unlabeled training and test data were categorized into three energy ranges:0–25 keV,25–100 keV,and 100–2100 keV.Second,GMM-KNN achieved small-batch clustering in three energy intervals with only the tail integral Q_(tail) and total integral Q_(total) as the pulse features.Subsequently,we selected the pulses with a probability greater than 99%from the GMM clustering results to construct the training set.Finally,we improved the KNN algorithm such that GMM-KNN realized the classification and regression algorithms through the LabVIEW language.The outputs of GMM-KNN were the category or regression predictions.The proposed GMM-KNN constructed the training set using unlabeled real pulse data and realized n-γdiscrimination of ^(241)Am-Be pulses using the LabVIEW program.The experimental results demonstrated the high robustness and flexibility of GMM-KNN.Even when using only 1/4 of the training set,the execution time of GMM-KNN was only 2021 ms,with a difference of only 0.13%compared with the results obtained on the full training set.Furthermore,GMM-KNN outperformed the charge comparison method in terms of accuracy,and correctly classified 5.52%of the ambiguous pulses.In addition,the GMM-KNN regressor achieved a higher figure of merit(FOM),with FOM values of 0.877,1.262,and 1.020,corresponding to the three energy ranges,with a 32.08%improvement in 0–25 keV.In conclusion,the GMM-KNN algorithm demonstrates accurate and readily deployable real-time n-γdiscrimination performance,rendering it suitable for on-site analysis.展开更多
文摘在定位请求服务中,如何保护用户的位置隐私和位置服务提供商(Localization service provider,LSP)的数据隐私是关系到WiFi指纹定位应用的一个具有挑战性的问题。基于密文域的K-近邻(K-nearest neighbors,KNN)检索,本文提出了一种适用于三方的定位隐私保护算法,能有效提升对LSP指纹信息隐私的保护强度并降低计算开销。服务器和用户分别完成对指纹信息和定位请求的加密,而第三方则基于加密指纹库和加密定位请求,在隐私状态下完成对用户的位置估计。所提算法把各参考点的位置信息随机嵌入指纹,可避免恶意用户获取各参考点的具体位置;进一步利用布隆滤波器在隐藏接入点信息的情况下,第三方可完成参考点的在线匹配,实现对用户隐私状态下的粗定位,可与定位算法结合降低计算开销。在公共数据集和实验室数据集中,对两种算法的安全、开销和定位性能进行了全面的评估。与同类加密算法比较,在不降低定位精度的情况下,进一步增强了对数据隐私的保护。
基金supported by National Science Fund for Distinguished Young Scholars of China(No.12205062).
文摘Machine learning algorithms are considered as effective methods for improving the effectiveness of neutron-gamma(n-γ)discrimination.This study proposed an intelligent discrimination method that combined a Gaussian mixture model(GMM)with the K-nearest neighbor(KNN)algorithm,referred to as GMM-KNN.First,the unlabeled training and test data were categorized into three energy ranges:0–25 keV,25–100 keV,and 100–2100 keV.Second,GMM-KNN achieved small-batch clustering in three energy intervals with only the tail integral Q_(tail) and total integral Q_(total) as the pulse features.Subsequently,we selected the pulses with a probability greater than 99%from the GMM clustering results to construct the training set.Finally,we improved the KNN algorithm such that GMM-KNN realized the classification and regression algorithms through the LabVIEW language.The outputs of GMM-KNN were the category or regression predictions.The proposed GMM-KNN constructed the training set using unlabeled real pulse data and realized n-γdiscrimination of ^(241)Am-Be pulses using the LabVIEW program.The experimental results demonstrated the high robustness and flexibility of GMM-KNN.Even when using only 1/4 of the training set,the execution time of GMM-KNN was only 2021 ms,with a difference of only 0.13%compared with the results obtained on the full training set.Furthermore,GMM-KNN outperformed the charge comparison method in terms of accuracy,and correctly classified 5.52%of the ambiguous pulses.In addition,the GMM-KNN regressor achieved a higher figure of merit(FOM),with FOM values of 0.877,1.262,and 1.020,corresponding to the three energy ranges,with a 32.08%improvement in 0–25 keV.In conclusion,the GMM-KNN algorithm demonstrates accurate and readily deployable real-time n-γdiscrimination performance,rendering it suitable for on-site analysis.
