Recent research has studied the role of sparsity in high dimensional regression and signal reconstruction, establishing theoretical limits for recovering sparse models from sparse...
Shuheng Zhou, John D. Lafferty, Larry A. Wasserman
Learning is a task that generalizes many of the analyses that are applied to collections of data, and in particular, collections of sensitive individual information. Hence, it is n...
Preserving individual privacy when publishing data is a problem that is receiving increasing attention. According to the k-anonymity principle, each release of data must be such th...
In this paper, we present a new approach to performing important classes of genomic computations (e.g., search for homologous genes) that makes a significant step towards privacy...
The recent explosion in shared media content and sensed data produced by mobile end-users is challenging well-established principles and assumptions in data trust models. A fundam...
Vincent Lenders, Emmanouil Koukoumidis, Pei Zhang,...