摘要
随着网络信息技术的快速发展,社交网络迅速涌现。针对社交网络隐私保护问题,提出了一种基于GSNPP算法的隐私保护方法。它通过对社交网络中节点进行聚类,再对生成的簇进行簇内泛化及簇间泛化,来对社交网络进行匿名化处理,拟达到隐私保护的目的;同时量化了社交网络匿名化处理过程中所带来的不同类型信息的丢失。最后通过实验验证了该方法的可行性和有效性。
With the rapid development of network information technology, social networks have sprung up. For the protection of their privacy, we proposed a GSNPP algorithm-based privacy-preserving approach. By clustering the nodes in social networks, and then making generalization not only within cluster but also among clusters, the approach processes the social networks anonymously, to achieve the purpose of privacy protection, then quantifies the information loss of different types in the process of the anonymity of social networks. At last, the results of experiment demonstrate the feasibility and validity of the approach.
出处
《计算机科学》
CSCD
北大核心
2012年第3期104-106,共3页
Computer Science
基金
重庆市信息产业发展资金项目(200921011)资助
关键词
社交网络
隐私保护
聚类方法
K-匿名
Social networks, Privacy preserve, Cluster approach, k-anonymity
