A standardised differential privacy framework for epidemiological modeling with mobile phone data.

ABSTRACT: During the COVID-19 pandemic, the use of mobile phone data for monitoring human mobility patterns has become increasingly common, both to study the impact of travel restrictions on population movement and epidemiological modeling. Despite the importance of these data, the use of location information to guide public policy can raise issues of privacy and ethical use. Studies have shown that simple aggregation does not protect the privacy of an individual, and there are no universal standards for aggregation that guarantee anonymity. Newer methods, such as differential privacy, can provide statistically verifiable protection against identifiability but have been largely untested as inputs for compartment models used in infectious disease epidemiology. Our study examines the application of di

SUBMITTER: Savi MK 

PROVIDER: S-EPMC10610440 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature