Hiep H. Nguyen

Learn More
The problem of private publication of graph data has attracted a lot of attention recently. The prevalence of differential privacy makes the problem more promising. However, a large body of existing works on differentially private release of graphs have not answered the question about the upper bounds of privacy budgets. In this paper, for the first time,(More)
Rather than anonymizing social graphs by generalizing them to super nodes/edges or adding/removing nodes and edges to satisfy given privacy parameters, recent methods exploit the semantics of uncertain graphs to achieve privacy protection of participating entities and their relationships. These techniques anonymize a deterministic graph by converting it(More)
Currently, most of the online social networks (OSN) keep their data secret and in centralized manner. Researchers are allowed to crawl the underlying social graphs (and data) but with limited rates, leading to only partial views of the true social graphs. To overcome this constraint, we may start from user perspective, the contributors of the OSNs. More(More)
—Complex networks usually expose community structure with groups of nodes sharing many links with the other nodes in the same group and relatively few with the nodes of the rest. This feature captures valuable information about the organization and even the evolution of the network. Over the last decade, a great number of algorithms for community detection(More)
This position paper first summarizes work done by the first author on location privacy and differential privacy. These techniques will help to solve privacy problems in decentralized mobile social networks, which is the main theme of his PhD research. The paper then briefly reviews the state-of-the-art in privacy-preservation of social graphs and clarifies(More)
  • 1