[November, 2013] Former Rutgers graduate student Darakhshan Mir

differential privacy to human mobility modeling in metropolitan areas. Models that can faithfully mimic human mobility have broad applicability in public planning, ecology, epidemiology, and other fields. The research of Mir and her collaborators adapts a previously-existing approach for metropolitan mobility modeling using data from cellular phone networks to add privacy guarantees. The goal of the new work is to realistically model how large populations move within a metropolitan area while rigorously safeguarding the privacy of individuals whose data are used. The previous approach, called WHERE, takes as input spatial and temporal probability distributions drawn from empirical data, such as Call Detail Records (CDRs), and produces synthetic CDRs for a synthetic population as output. The synthetic output captures distinct mobility patterns that arise because of differences in geographic distributions of homes and jobs, transportation infrastructures, and other factors. Its accuracy has been validated against billions of location samples for hundreds of thousands of cell phones in the New Yor