Geoprivacy

Location privacy has been a topic of research for many years but has recently experienced a resurgence in public interest. This renewed interest is driven by recent advances in location-enabled devices, sensors and context-aware technology, and the rise of the Internet of Things (IoT). The data generated via these sensors and devices is being collected,analyzed, and synthesized at an unprecedented rate. While much of this data is used in the advancement of products or services (e.g., navigation technologies), many individuals remain unaware of the information that is being collected, how it is being collected, and more importantly, how it is being used. The resulting information extracted from this personal data has contributed to significant advances in the computational and geospatial sciences, e.g., location recommendations or health services. However, these advances often come at the high cost of reduced location privacy.

Read some of our publications on the topic:

McKenzie, G., Romm, D., Zhang, H., Brunila, M. (2022) PrivyTo: A privacy preserving location sharing platform. Transactions in GIS. Wiley Press.

Zhang, H., McKenzie, G. (2022) Rehumanize geoprivacy: From disclosure control to human perception. GeoJournal. https://dx.doi.org/https://doi.org/10.1007/s10708-022-10598-4

McKenzie, G., Keßler C., Andris, C. (2019) Editorial for our special issue in the Journal of Spatial Information Science (JOSIS) on Geospatial Privacy and Security. https://www.josis.org/index.php/josis/article/view/608/261

Keßler, C., McKenzie, G. (2017). A Geoprivacy Manifesto. Transactions in GIS. 22(1) 3-19. Wiley Press. https://grantmckenzie.com/academics/GeoprivacyManifesto2017.pdf

McKenzie, G., Janowicz, J., Seidl, D. (2016) Geo-privacy beyond coordinates In: Sarjakoski T., Santos M.Y., Sarjakoski, L.T. (eds) Geospatial Data in a Changing World. AGILE 2016. (pp. 157-175) Springer. https://grantmckenzie.com/academics/McKenzie_AGILE2016.pdf

Qiang, Dan; McKenzie, Grant

Navigating the Post-Pandemic Urban Landscape: Disparities in Transportation Recovery & Regional Insights from New York City Journal Article

In: Computers, Environment and Urban Systems, vol. 110, pp. 102111, 2024.

Abstract | Links | BibTeX

Verma, Priyanka; McKenzie, Grant

Regional Comparison of Socio-Demographic Variation in Urban E-scooter Usage Journal Article

In: Environment and Planning B: Urban Analytics and City Science, 2024.

Abstract | Links | BibTeX

@article{Verma2024,

title = {Regional Comparison of Socio-Demographic Variation in Urban E-scooter Usage},

author = {Priyanka Verma and Grant McKenzie},

url = {https://grantmckenzie.com/academics/Verma_2024.pdf},

year  = {2024},

date = {2024-03-26},

urldate = {2024-03-09},

journal = {Environment and Planning B: Urban Analytics and City Science},

abstract = {In recent years we have witnessed explosive growth in the shared, free-floating, electric scooter industry. While still controversial in many North American cities, a number of large e-scooter operators have managed to carve out a piece of the urban transportation landscape. As these vehicles shift from novelty services to increasingly reliable modes of short personal travel, the discussion has turned to investigating who exactly benefits from these micromobility services and who are being left behind. Though population surveys have been administered to identify the socio-demographic characteristics of e-scooter riders in the past, little work has linked these characteristics through trips, or investigated the regional variation in these demographic factors. In this work we explore the variability and similarities in e-scooter rider characteristics across three major U.S. cities. To accomplish this, we apply a Moran’s Eigenvector Spatial Filtering linear regression model and compare our results to more commonly used spatial regression approaches. Our results indicate that the spatial filtering approach outperforms other methods in identifying socio-demographic characteristics of e-scooter users, across multiple regions. We find that many socio-demographics associated with e-scooter usage are regionally variant, despite younger users making up the core user base in all cities. There are variations in usage based on gender, income, and race across cities with Black and Hispanic populations remaining underserved. The implications of these findings are discussed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Brunila, Mikael; LaViolette, Jack; CH-Wang, Sky; Verma, Priyanka; Féré, Clara; McKenzie, Grant

Toward a Critical Toponymy Framework for Named Entity Recognition: A Case Study of Airbnb in New York City Best Paper Proceedings Article

In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP2023), 2023.

Abstract | Links | BibTeX

McKenzie, Grant; Zhang, Hongyu

Platial k-anonymity: Improving location anonymity through temporal popularity signatures Best Paper Proceedings Article

In: Proceedings of the 12th International Conference on Geographic Information Science, Schloss Dagstuhl -- Leibniz-Zentrum fur Informatik, 2023, ISSN: 1868-8969.

Abstract | Links | BibTeX

@inproceedings{McKenzie2023b,

title = {Platial k-anonymity: Improving location anonymity through temporal popularity signatures},

author = {Grant McKenzie and Hongyu Zhang },

url = {https://drops.dagstuhl.de/opus/volltexte/2023/18904/},

issn = {1868-8969},

year  = {2023},

date = {2023-08-31},

urldate = {2023-08-31},

booktitle = {Proceedings of the 12th International Conference on Geographic Information Science},

volume = {277},

publisher = {Schloss Dagstuhl -- Leibniz-Zentrum fur Informatik},

series = {Leibniz International Proceedings in Informatics (LIPIcs)},

abstract = {While it is increasingly necessary in today's digital society, sharing personal location information comes at a cost.  Sharing one's precise place of interest, e.g., Compass Coffee, enables a range of location-based services, but substantially reduces the individual's privacy.  Methods have been developed to obfuscate and anonymize location data while still maintaining a degree of utility.  One such approach, spatial k-anonymity, aims to ensure an individual's level of anonymity by reporting their location as a set of k potential locations rather than their actual location alone.  Larger values of k increase spatial anonymity while decreasing the utility of the location information.  Typical examples of spatial k-anonymized datasets present elements as simple geographic points with no attributes or contextual information.  In this work, we demonstrate that the addition of publicly available contextual data can significantly reduce the anonymity of a k-anonymized dataset.  Through the analysis of place type temporal visitation patterns, hours of operation, and popularity values, one's anonymity can decreased by more than 50 percent.  We propose a platial k-anonymity approach that leverages a combination of temporal popularity signatures and report the amount that k must increase in order to maintain a certain level of anonymity. Finally, a method for reporting platial k-anonymous regions is presented and the implications of our methods are discussed.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}