As an integral part of data security, identity disclosureis a major privacy breach, which reveals the identification of entities with certain background knowledge known by an adversary. Most recent studies on this problem focus on the protection of relational data or simple graph data (i.e. undirected, un weighted and acyclic). However, a weighted graph can introduce much more unique information than its simple version, which makes the disclosure easier. As more real-world graphs or social networks are released publicly, there is growing concern about privacy breaching for the entities involved. In this paper, we first formalize a general anonymizing model to deal with weight-related attacks, and discuss an efficient metric to quantify information loss incurred in the perturbation. Then we consider a very practical attack based on the sum of adjacent weights for each vertex, which is known as volume in graph theory field. We also propose a complete solution for the weight anonymization problem to prevent a graph from volume attack. Our approaches are efficient and practical, and have been validated by extensive experiments on both synthetic and real-world datasets.
[Show abstract][Hide abstract] ABSTRACT: Privacy preserving analysis of a social network aims at a better understanding of the network and its behavior, while at the same time protecting the privacy of its individuals. We propose an anonymization method for weighted graphs, i.e., for social networks where the strengths of links are important. This is in contrast with many previous studies which only consider unweighted graphs. Weights can be essential for social network analysis, but they pose new challenges to privacy preserving network analysis. In this paper, we mainly consider prevention of identity disclosure, but we also touch on edge and edge weight disclosure in weighted graphs. We propose a method that provides k-anonymity of nodes against attacks where the adversary has information about the structure of the network, including its edge weights. The method is efficient, and it has been evaluated in terms of privacy and utility on real word datasets.
Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference on; 01/2012
[Show abstract][Hide abstract] ABSTRACT: Information breaches in social networks and other published data have caused many concerns of privacy issues in recent years. Since information in networks can be modeled as graphs, various techniques have been proposed to preserve privacy of sensitive data on directed/un-directed, weighted/un-weighted graphs. To preserve privacy on graphs, most works for publishing anonymized social networks have attempted to prevent unique patterns to be re-identified, such as, nodes, links, and sub-graphs. In this work, we study the problem of anonymizing sensitive shortest paths in graphs. We examine the concept of k-shortest path privacy, in which at least k indistinguishable shortest paths exist between specified sensitive source and destination vertices. Due to the overlaps of shortest paths, we propose three algorithms to modify three categories of edges to achieve the k-shortest path privacy. Numerical experiments showing the characteristics of the proposed algorithms are given. The results demonstrate that the proposed algorithms are all feasible to achieve k-shortest path privacy, with different degrees of execution times and information losses, and can be served as design reference for shortest path anonymization.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.