Abstract
Models of human mobility have broad applicability in urban planning, ecology, epidemiology, and other fields. Starting with Call Detail Records (CDRs) from a cellular telephone network that have gone through a straightforward anonymization procedure, the prior WHERE modeling approach produces synthetic CDRs for a synthetic population. The accuracy of WHERE has been validated against billions of location samples for hundreds of thousands of cell phones in the New York and Los Angeles metropolitan areas. In this work, we introduce DP-WHERE, which modifies WHERE by adding controlled noise to achieve differential privacy. We present experiments showing that the accuracy of DP-WHERE remains close to that of WHERE and of real CDRs. With this work, we aim to enable the creation and possible release of synthetic models that capture the mobility patterns of real metropolitan populations while preserving privacy.
This is joint work with Darakhshan Mir, Sibren Isaacman, Ramón Cáceres, and Margaret Martonosi, and appears in the 2013 IEEE International Conference on Big Data.