DP-WHERE: Differentially private modeling of human mobility

Darakhshan J. Mir, Sibren Isaacman, Ramon Caceres, Margaret Martonosi, Rebecca N. Wright

Research output: Chapter in Book/Report/Conference proceedingConference contribution

65 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 IEEE International Conference on Big Data, Big Data 2013
Pages580-588
Number of pages9
DOIs
StatePublished - Dec 1 2013
Event2013 IEEE International Conference on Big Data, Big Data 2013 - Santa Clara, CA, United States
Duration: Oct 6 2013Oct 9 2013

Publication series

NameProceedings - 2013 IEEE International Conference on Big Data, Big Data 2013

Other

Other2013 IEEE International Conference on Big Data, Big Data 2013
CountryUnited States
CitySanta Clara, CA
Period10/6/1310/9/13

All Science Journal Classification (ASJC) codes

  • Software

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