Location Data and COVID-19 Contact Tracing: How Data Privacy Regulations and Cell Service Providers Work In Tandem

Callie, Monroe,; Tazi, Faiza; Das, Sanchari

doi:10.2139/ssrn.3812765

Cited by 6 publications

(2 citation statements)

References 46 publications

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“…Consumers’ data protection and privacy are vital in today’s technology era, therefore governments, healthcare providers, and business groups have been using digital tracking to keep COVID-19 outbreaks under control. Although this method has the potential to minimize pandemic transmission, it has significant privacy implications ( Sharma et al, 2020 ; Monroe, Tazi & Das, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Data privacy during pandemics: a systematic literature review of COVID-19 smartphone applications

Alshawi

Al-Razgan

Alkallas

et al. 2022

PeerJ Computer Science

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Background On January 8, 2020, the Centers for Disease Control and Prevention officially announced a new virus in Wuhan, China. The first novel coronavirus (COVID-19) case was discovered on December 1, 2019, implying that the disease was spreading quietly and quickly in the community before reaching the rest of the world. To deal with the virus’ wide spread, countries have deployed contact tracing mobile applications to control viral transmission. Such applications collect users’ information and inform them if they were in contact with an individual diagnosed with COVID-19. However, these applications might have affected human rights by breaching users’ privacy. Methodology This systematic literature review followed a comprehensive methodology to highlight current research discussing such privacy issues. First, it used a search strategy to obtain 808 relevant papers published in 2020 from well-established digital libraries. Second, inclusion/exclusion criteria and the snowballing technique were applied to produce more comprehensive results. Finally, by the application of a quality assessment procedure, 40 studies were chosen. Results This review highlights privacy issues, discusses centralized and decentralized models and the different technologies affecting users’ privacy, and identifies solutions to improve data privacy from three perspectives: public, law, and health considerations. Conclusions Governments need to address the privacy issues related to contact tracing apps. This can be done through enforcing special policies to guarantee users privacy. Additionally, it is important to be transparent and let users know what data is being collected and how it is being used.

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Section: Discussionmentioning

confidence: 99%

Data privacy during pandemics: a systematic literature review of COVID-19 smartphone applications

Alshawi

Al-Razgan

Alkallas

et al. 2022

PeerJ Computer Science

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“…This data applies to model training for various services, e.g. for destination prediction [4] or contact tracing during Covid-19 [5] but can expose critical personal information about the user, such as home or work locations. Subsequently, several methods were recently developed to generate artificial and personal trajectory data.…”

Section: Introductionmentioning

confidence: 99%

Privacy Preserving Human Mobility Generation using Grid based Data and Graph Autoencoders

Netzler,

Lienkamp

2024

Preprint

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The proposed method deals with the problem of data privacy and sharing when processing personal mobility tracking data. Previous methods concentrate on producing highly detailed data on short-term and restricted areas, e.g. for autonomous driving scenarios. Another possibility consists of city-wide scales and beyond, that are used to predict general traffic flows. The presented approach takes the tracked mobility behavior of individuals to create coherent new mobility data that reflects the long-term mobility behavior of the person, guaranteeing location persistency and sound embedding within the point-of-interest structure of the observed area. After an analysis and clustering step with the original data, the area is distributed into a geospatial grid structure (H3 is used here), and the neighborhood relationships between the grids are interpreted as a graph. A feed-forward-autoencoder and a graph encoding-decoding network generate a latent space representation of the area. The original clustered data is associated with their respective H3 grids. With a greedy algorithm approach and concerning privacy strategies, new combinations of grids are top-level patterns for individual mobility behavior. Concrete locations are found and connected within the grids based on the original data. The described method is then applied to a study with 1000 participants from the city of Munich in Germany, and the results are described, showing the application of the approach in generating synthetic data, enabling further research on individual mobility behavior and patterns. The result is a sharable dataset on the same abstraction level as the input data, which makes it interesting, particularly for machine learning applications.

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Network-side digital contact tracing on a large university campus

Malloy¹,

Hartung²,

Wangen³

et al. 2022

Proceedings of the 28th Annual International Conference on Mobile Computing and Networking

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Location Data and COVID-19 Contact Tracing: How Data Privacy Regulations and Cell Service Providers Work In Tandem

Cited by 6 publications

References 46 publications

Data privacy during pandemics: a systematic literature review of COVID-19 smartphone applications

Data privacy during pandemics: a systematic literature review of COVID-19 smartphone applications

Privacy Preserving Human Mobility Generation using Grid based Data and Graph Autoencoders

Network-side digital contact tracing on a large university campus

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