# A Differentiated Anonymity Algorithm for Social Network Privacy Preservation
###### Abstract  
Devising methods to publish social network data in a form that affords utility without compromising privacy remains a longstanding challenge, while many existing methods based on \(k\)-anonymity algorithms on social networks may result in nontrivial utility loss without analyzing the social network topological structure and without considering the attributes of sparse distribution. Toward this objective, we explore the impact of the attributes of sparse distribution on data utility. Firstly, we propose a new utility metric that emphasizes network structure distortion and attribute value loss. Furthermore, we design and implement a differentiated \(k\)-anonymity \(l\)-diversity social network anonymity algorithm, which seeks to protect users' privacy in social networks and increase the usability of the published anonymized data. Its key idea is that it divides a node into two child nodes and only anonymizes sensitive values to satisfy anonymity requirements. The evaluation results show that our method can effectively improve the data utility as compared to generalized anonymizing algorithms. social network; privacy; data utility; anonymity; differentiated +
Footnote †: journal:  
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attribute distribution is relatively sparse in social networks. Additionally, in realistic social networks, almost every attribute has a rich diversity of values, only some of which are sensitive. It is claimed that existing privacy-preserving algorithms do not distinguish between the sensitivity of privacy attribute values, therefore, non-sensitive information is altered and this leads to a lower usability of the published data [6; 11]. For example, as depicted in Figure 1, the human disease network has various values. Most people generally consider the disease HIV as private, while considering the disease cold as non-sensitive information. Meanwhile, node V3 does not mark health status. Thus, the human disease network in Figure 1 is sparse and the existing methods may lead to excessive data utility loss for published data. To tackle this issue, we explored the attributes' sparse distribution and studied differentiated sensitivity of varied attribute values, proposing a finer granular anonymization algorithm that shifts anonymous objects from the attribute to some sensitive attribute values. In addition, most previous works take the total number of modified edges or modified nodes as the only metric to measure the social network utility loss without considering the graph structure interference. The lack of exploring utility is partially responsible for the high rate of structure interference in publishing data and contributes to the decrease of data availability. To approach this dearth of research on anonymized data utility for anonymity algorithms, we have undertaken to develop a proper utility function targeted at measuring both information loss and network structure interference that have been shown to be essential for data integrity and authenticity. To date, many researchers have put forward a variety of personalized privacy protection models and algorithms to improve the published data utility. These personalized anonymous models almost insist that individuals in social networks have different privacy protection requirements and put users who have different privacy protection needs into different anonymous equivalence group. For example, [PERSON] et al. [12] divided user privacy protection needs into S (ultra), H (high), M (middle), and L (low) levels, then put forward a personalized _k_-degree-_l_-diversity anonymity model. [PERSON] [13] proposed a personal privacy protection scheme based on a social network subset.