Utility-aware social network graph anonymization

As the need for social network data publishing continues to increase, how to preserve the privacy of the social network data before publishing is becoming an important and challenging issue. A common approach to address this issue is through anonymization of the social network structure. The problem with altering the structure of the links relationship in social network data is how to balance between the gain of privacy and the loss of information (data utility). In this paper, we address this problem. We propose a utility-aware social network graph anonymization. The approach is based on a new metric that calculates the utility impact of social network link modification. The metric utilizes the shortest path length and the neighborhood overlap as the utility value. The value is then used as a weight factor in preserving structural integrity in the social network graph anonymization. For any modification made to the social network links, the proposed approach guarantees that the distance between vertices in the modified social network stays as close as the original social network graph prior to the modification. Experimental evaluation shows that the proposed metric improves the utility preservation as compared to the number-of-change metric.