We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites ha...
In recent years, the wide availability of personal data has made the problem of privacy preserving data mining an important one. A number of methods have recently been proposed fo...
We propose PASTE, the first differentially private aggregation algorithms for distributed time-series data that offer good practical utility without any trusted server. PASTE add...
An increasing number of high-tech devices, such as driver monitoring systems and Internet usage monitoring tools, are advertised as useful or even necessary for good parenting of ...
In this paper, we introduce the concept of witness anonymity for peer-to-peer systems. Witness anonymity combines the seemingly conflicting requirements of anonymity (for honest p...