社会网络数据的k-匿名发布

由于科学研究和数据共享等需要，应该发布社会网络数据。但直接发布社会网络数据会侵害个体隐私，在发布数据的同时要进行隐私保护。针对将邻域信息作为背景知识的攻击者进行目标节点识别攻击的场景提出了基于k-匿名发布的隐私保护方案。根据个体的隐私保护要求设立不同的隐私保护级别，以最大程度地共享数据，提高数据的有效性。设计实现了匿名发布的KNP算法，并在数据集上进行了验证，实验结果表明该算法能够有效抵御部域攻击。

Social Networks Data Publication Based on k-anonymity

Because of scientific researching and data sharing, social networks data should be released. However, individual privacy will be breached if social networks data will be published directly. Therefore, privacy protection should be carried on while releasing social networks data. A privacy protection method based on k-anonymity was proposed. The method is suitable for the scene that the aggressor with background knowledge of neighborhood information wants to reidentify the target node in published social networks. According to the individual privacy protection rectuirement, the entities set different levels of privacy protection to share data and improve data utility as possible as. Designed and implemented the KNP algorithm to publish data anonymously and carry on experiment on dataset to validate the algorithm. Experimental results show that the algorithm can effectively resist the neighborhood attack.