计算机科学 ›› 2017, Vol. 44 ›› Issue (Z6): 395-398.doi: 10.11896/j.issn.1002-137X.2017.6A.089
彭慧丽,张啸剑,金凯忠
PENG Hui-li, ZHANG Xiao-jian and JIN Kai-zhong
摘要： 基于用户朋友关系的社交网络项目推荐技术可能泄露用户-项目隐私偏好。传统的匿名化方法由于过分依赖特定知识背景假设 而存在内在的脆弱性。提出一种基于差分隐私的社交网络项目推荐方法DPSR,该方法利用聚类技术对用户进行划分,利用拉普拉斯机制对用户-项目边的权重进行扰动。为了克服边权重中异常点对推荐结果的影响,提出了一种基于k-中心点的边权重聚类方法,该方法利用指数机制挑选出类中边权重集合的中位数。实验结果表明,DPSR优于同类方法。
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