【摘要】 社会网络数据发布可能导致隐私泄露，攻击者可以利用背景知识推断出节点的身份。为减少信息损失，提出一种基于社会网络节点目标度的k度匿名隐私保护方法。该方法先将节点的度序列按非递增序排序，使用动态规划算法对度序列分组，计算组内匿名代价最小的目标度，将组内节点的度都修改为匿名代价最小的目标度，构造k度匿名序列。然后使用优先级构造方法，选择优先保留原始图中存在的边构造k度匿名图。实验结果表明，该方法信息损失较小，成功抵御以节点度为背景知识的身份识别攻击，实现了社会网络隐私保护。更多还原

【Abstract】 Social network data publishing can lead to privacy breaches, and attackers can use background knowledge to infer the identity of nodes. In order to reduce information loss, this paper presents a k degree anonymous privacy protection method based on the objective degree of social network nodes. The method first sorts the degree sequence of nodes in a non-increasing order, uses a dynamic programming algorithm to group the degree sequence, calculates the objective degree with the lowest anonymity cost in the group, changes the degree of nodes in the group to the objective degree with the lowest anonymity cost, and constructs the k degree anonymous sequence. Then, using the priority construction method, the k degree anonymous graph is constructed by preferentially preserving the existing edges in the original graph. The experimental results show that the proposed method has less information loss,successfully resists the identity attack based on node degree as background knowledge, and realizes the protection of social network privacy.更多还原