隐私保护真值发现技术在移动群智感知网络领域中受到了广泛关注.然而在实际应用中,恶意用户上传的异常值对真值发现结果的可靠性带来了较大影响.为此,提出了一种基于区间验证的隐私保护真值发现算法IVPPTD (Interval Verification based Privacy-Preserving Truth Discovery).首先,采用Paillier同态加密方法实现用户感知数据的安全上传和真值发现,保护用户的感知数据、权重信息以及估算真值的隐私不被泄露.其次,提出一种密文域中的异常数据过滤算法,对数据约束区间外的异常值进行数据清洗,从而在保护用户敏感信息不被泄露的前提下,提高真值发现结果的可靠性.最后,基于感知平台和密钥生成中心协作完成真值发现过程,减少了用户与云服务器之间的通信开销.仿真实验结果表明,所提方法具有高准确率、对异常值的鲁棒性以及较低的计算开销.
针对传统的k-means算法的聚类数目k无法确定、初始聚类中心随机给定、容易受到离群点影响等问题,该算法使用LOF (Local Outlier Factor)离群点检测算法计算数据集中每个数据对象的离群因子,并去除离群因子大于指定阈值的数据对象,使用手肘法来确定符合数据集的最佳k值,根据最大密度和最大距离的思想结合每个点的离群因子来选取初始聚类中心并进行后续聚类中心的迭代,聚类完成后结合三支决策的思想对聚类结果的每个簇内的数据对象进行进一步优化。实验结果表明ODT-kmeans算法能合理选取k值、减少离群点的影响并且可以消除随机选择初始聚类中心的问题,提高了k-means聚类算法的准确率。In view of the problems of the traditional k-means algorithm, such as the number of clusters k cannot be determined, the initial cluster center is randomly given, and it is easily affected by outliers, this algorithm uses the LOF (Local Outlier Factor) outlier detection algorithm to calculate the outlier factor of each data object in the data set and remove the data objects whose outlier factor is greater than the specified threshold. The elbow method is used to determine the best k value that meets the data set. The initial cluster center is selected based on the idea of maximum density and maximum distance combined with the outlier factor of each point and the subsequent cluster center iterations are performed. After clustering is completed, the idea of three-way decision is combined to further optimize the data objects in each cluster of the clustering results. Experimental results show that the ODT-kmeans algorithm can reasonably select the k value, reduce the influence of outliers, and eliminate the problem of randomly selecting the initial cluster center, thereby improving the accuracy of the k-means clustering algorithm.
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