Privacy-Preserving Travel Time Prediction With Uncertainty Using GPS Trace Data

Liu, Fang; Wang, Dong; Wang, Chao

doi:10.1109/tmc.2021.3074865

Cited by 10 publications

(2 citation statements)

References 48 publications

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“…The approach we propose for releasing privacy-preserving location information is the doppelganger [ 41 ], based on the GI concept. The main idea behind doppelganger, as suggested by the name, is to release sanitized versions of the true location P via the planar Laplace mechanism so to satisfy GI guarantees.…”

Section: Methodsmentioning

confidence: 99%

“…We examine a privacy-preserving approach to releasing location data based on GI. We focus on releasing cross-sectional location data at a given time point rather than travel trajectories [ 41 ], the latter being a topic for future research. Even though released data are cross-sectional, they can be released on a regular time basis, e.g., every day or every 3 days, allowing temporal examination of certain trends.…”

Section: Privacy-preserving Release Of Case Location Datamentioning

confidence: 99%

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Some examples of privacy-preserving sharing of COVID-19 pandemic data with statistical utility evaluation

Liu

Wang

Tian

2023

BMC Med Res Methodol

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Background A considerable amount of various types of data have been collected during the COVID-19 pandemic, the analysis and understanding of which have been indispensable for curbing the spread of the disease. As the pandemic moves to an endemic state, the data collected during the pandemic will continue to be rich sources for further studying and understanding the impacts of the pandemic on various aspects of our society. On the other hand, naïve release and sharing of the information can be associated with serious privacy concerns. Methods We use three common but distinct data types collected during the pandemic (case surveillance tabular data, case location data, and contact tracing networks) to illustrate the publication and sharing of granular information and individual-level pandemic data in a privacy-preserving manner. We leverage and build upon the concept of differential privacy to generate and release privacy-preserving data for each data type. We investigate the inferential utility of privacy-preserving information through simulation studies at different levels of privacy guarantees and demonstrate the approaches in real-life data. All the approaches employed in the study are straightforward to apply. Results The empirical studies in all three data cases suggest that privacy-preserving results based on the differentially privately sanitized data can be similar to the original results at a reasonably small privacy loss ($$\epsilon \approx 1$$ ϵ ≈ 1 ). Statistical inferences based on sanitized data using the multiple synthesis technique also appear valid, with nominal coverage of 95% confidence intervals when there is no noticeable bias in point estimation. When $$\epsilon <1$$ ϵ < 1 and the sample size is not large enough, some privacy-preserving results are subject to bias, partially due to the bounding applied to sanitized data as a post-processing step to satisfy practical data constraints. Conclusions Our study generates statistical evidence on the practical feasibility of sharing pandemic data with privacy guarantees and on how to balance the statistical utility of released information during this process.

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Section: Methodsmentioning

confidence: 99%

Section: Privacy-preserving Release Of Case Location Datamentioning

confidence: 99%