On Identity Disclosure in Weighted Graphs
ABSTRACT 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.
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ABSTRACT: In a social network, nodes correspond to people or other social en- tities, and edges correspond to social links between them. In an effort to preserve privacy, the practice of anonymization replaces names with meaningless unique identifiers. We describe a family of attacks such that even from a single anonymized copy of a social network, it is possible for an adversary to learn whether edges exist or not between specific targeted pairs of nodes.01/2007
Conference Proceeding: Preserving Privacy in Social Networks Against Neighborhood Attacks[show abstract] [hide abstract]
ABSTRACT: Recently, as more and more social network data has been published in one way or another, preserving privacy in publishing social network data becomes an important concern. With some local knowledge about individuals in a social network, an adversary may attack the privacy of some victims easily. Unfortunately, most of the previous studies on privacy preservation can deal with relational data only, and cannot be applied to social network data. In this paper, we take an initiative towards preserving privacy in social network data. We identify an essential type of privacy attacks: neighborhood attacks. If an adversary has some knowledge about the neighbors of a target victim and the relationship among the neighbors, the victim may be re-identified from a social network even if the victim's identity is preserved using the conventional anonymization techniques. We show that the problem is challenging, and present a practical solution to battle neighborhood attacks. The empirical study indicates that anonymized social networks generated by our method can still be used to answer aggregate network queries with high accuracy.Data Engineering, 2008. ICDE 2008. IEEE 24th International Conference on; 05/2008
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ABSTRACT: It will be shown that for almost all weights one can reconstruct a weighted graph from its spectrum. This result is the opposite to the well-known theorem of Botti and Merris which states that reconstruction of non-weighted graphs is, in general, impossible since almost all (non-weighted) trees share their spectrum with another non-isomorphic tree.European Journal of Combinatorics. 01/2000;