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|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 paper, we introduce DP-WHERE, which modifies WHERE by adding controlled noise to achieve differential privacy, a strict definition of privacy that makes no assumptions about the power or background knowledge of a potential adversary. We also 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.|
|Citation:||Mir, Darakhshan J., Sibren Isaacman, Ramón Cáceres, Margaret Martonosi, and Rebecca N. Wright. "DP-WHERE: Differentially private modeling of human mobility." In IEEE International Conference on Big Data (2013): pp. 580-588. doi:10.1109/BigData.2013.6691626|
|Pages:||580 - 588|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||IEEE International Conference on Big Data|
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