基于节点分割的社交网络属性隐私保护

现有研究表明，社交网络中用户的社交结构信息和非敏感属性信息均会增加用户隐私属性泄露的风险.针对当前社交网络隐私属性匿名算法中存在的缺乏合理模型、属性分布特征扰动大、忽视社交结构和非敏感属性对敏感属性分布的影响等弱点，提出一种基于节点分割的隐私属性匿名算法.该算法通过分割节点的属性连接和社交连接，提高了节点的匿名性，降低了用户隐私属性泄露的风险.此外，量化了社交结构信息对属性分布的影响，根据属性相关程度进行节点的属性分割，能够很好地保持属性分布特征，保证数据可用性.实验结果表明，该算法能够在保证数据可用性的同时，有效抵抗隐私属性泄露.

Attribute Privacy Preservation in Social Networks Based on Node Anatomy

Recent research shows that social structures or non-sensitive attributes of users can increase risks of user sensitive attribute disclosure in social networks. Most of the existing private attribute anonymization schemes have many defects, such as lack of proper model, too much distortion on attributes distribution, neglect social structure and non-sensitive attributes' influence on sensitive attributes. In this paper, an attribute privacy preservation scheme based on node anatomy is proposed. It allocates original node's attribute links and social links to new nodes to improve original node's anonymity, thus protects user from sensitive attribute disclosure. Meanwhile, it measures social structure influence on attribute distribution, and splits attributes according to attributes' correlations. Experimental results show that the proposed scheme can maintain high data utility and resist private attribute disclosure.