计算机科学 ›› 2019, Vol. 46 ›› Issue (10): 154-160.doi: 10.11896/jsjkx.180901749
周艺华, 张冰, 杨宇光, 侍伟敏
ZHOU Yi-hua, ZHANG Bing, YANG Yu-guang, SHI Wei-min
摘要： 随着社交网络的迅速发展,社交网络积累了大量的数据,它们在一定程度上反映了社会规律。针对如何在保证隐私安全的前提下挖掘出有效知识的问题,提出了基于聚类的社交网络隐私保护方法,该方法具有隐私保护力度自适应、匿名模型安全性和有效性高的特点。该方法基于用户信息和社交关系进行聚类,将社交网络中的所有节点根据节点间的距离聚类为至少包含k个节点的超点,并对超点进行匿名化处理。匿名后的超点能够有效地防范以节点属性隐私、子图结构等为背景知识的各类隐私攻击,使攻击者无法以大于1/k的概率来识别用户。根据聚类算法和社交网络的特点优化聚类过程中初始节点的选取算法和节点间距的计算方法;同时通过结合自适应思想,优化隐私保护力度的选取方法,有效地减少了信息损失,提高了数据有效性。在Matlab上使用不同的数据集进行实验验证,结果表明所提算法在信息损失和运行时间上均优于其他相关方法,进一步证明了它的有效性和安全性。
中图分类号:
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