Brian Thompson

TL;DR: This paper model social networks as undirected graphs and formally define privacy models, attack models for the anonymization problem, in particular an i-hop degree-based anonymizationProblem, and presents two new and efficient clustering methods for undirecting graphs: bounded t-means clustering and union-split clustering algorithms that group similar graph nodes into clusters with a minimum size constraint.

TL;DR: This work presents a solution in which service providers can collaboratively compute aggregate queries without gaining knowledge of intermediate results, and users can verify the results of their queries, relying only on their trust of the data owner.

TL;DR: This paper represents recommendation data as a bipartite graph, and identifies several attacks that can re-identify users and determine their item ratings, and develops a robust and efficient anonymization algorithm, Predictive Anonymization, to achieve privacy goals.

TL;DR: It is concluded that information theory metrics can quantitatively assess electrical communication among cardiomyocytes and may find clinical application in the identification of rhythm-specific treatments which are currently unmet by traditional electrocardiographic techniques.

TL;DR: In this paper, the Social H-index (SH-index) is proposed to reflect an individual's impact on the research community by redistributing the h-index score according to the individual's contributions through a collaborative team.