Abstract. The advent of social network sites in the last years seems to be a trend that will likely continue. What naive technology users may not realize is that the information they provide online is stored and may be used for various purposes. Researchers have pointed out for some time the privacy implications of massive data gathering, and effort has been made to protect the data from unauthorized disclosure. However, the data privacy research has mostly targeted traditional data models such as microdata. Recently, social network data has begun to be analyzed from a specific privacy perspective, one that considers, besides the attribute values that characterize the individual entities in the networks, their relationships with other entities. Our main contributions in this paper are a greedy algorithm for anonymizing a social network and a measure that quantifies the information loss in the anonymization process due to edge generalization.

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Abstract. Generalization hierarchies are frequently used in computer science, statistics, biology, bioinformatics, and other areas when less specific values are needed for data analysis. Generalization is also one of the most used disclosure control technique for anonymizing data. For numerical attributes, generalization is performed either by using existing predefined generalization hierarchies or a hierarchy-free model. Because hierarchy-free generalization is not suitable for anonymization in all possible scenarios, generalization hierarchies are of particular interest for data anonymization. Traditionally, these hierarchies were created by the data owner with help from the domain experts. But while it is feasible to construct a hierarchy of small size, the effort increases for hierarchies that have many levels. Therefore, new approaches of creating these numerical hierarchies involve their automatic/on-the-fly generation. In this paper we extend an existing method for creating on-the-fly generalization hierarchies, we present several existing information loss measures used to assess the quality of anonymized data, and we run a series of experiments that show that our new method improves over existing methods to automatically generate on-the-fly numerical generalization hierarchies.