We study differential privacy in a distributed setting where two parties would like to perform analysis of their joint data while preserving privacy for both datasets. Our results ...
Andrew McGregor, Ilya Mironov, Toniann Pitassi, Om...
The use of any modern computer system leaves unintended traces of expired data and remnants of users' past activities. In this paper, we investigate the unintended persistenc...
Patrick Stahlberg, Gerome Miklau, Brian Neil Levin...
We present a multiresolution framework, called Multi-Tetra framework, that approximates volume data with different levelsof-detail tetrahedra. The framework is generated through a...
—The ubiquity of mobile devices has brought forth the concept of participatory sensing, whereby ordinary citizens can now contribute and share information from the urban environm...
We address privacy-preserving classification problem in a distributed system. Randomization has been the approach proposed to preserve privacy in such scenario. However, this appr...