Platial Analysis Lab

Platial Lab at UCGIS Symposium

June 7, 2023

This week Prof. McKenzie was a panellist at the 2023 University Consortium for Geographic Information Science (UCGIS) symposium at Yale University…

New NSERC funding for homomorphic encryption research

May 30, 2023

The lab was recently awarded funding from NSERC to study homomorphic encryption techniques for analyzing, and preserving the privacy of, human mobility data…

Equitable, accessible, and sustainable mobility workshops

April 24, 2023

We welcome participation in two upcoming workshops to be held in conjunction with the 12th International Conference on Geographic Information Science…

Congratulations to honours student Maya Shyevitch

April 13, 2023

Lab member Maya Shyevitch presented her honours thesis work during McGill’s Sustainability, Science, and Society poster session today…

Platial Lab at AAG 2023

April 4, 2023

Two members of the lab attended the American Association of Geographers annual meeting in Denver, CO this year…

The 4th Spatial Data Science Symposium

March 23, 2023

This year the lab will be heavily involved in organizing the 4th Spatial Data Science Symposium with a hub at McGill University…

Platial Lab at SKI2023

February 20, 2023

Last week six members of the lab participated in the 8th conference on Spatial Knowledge and Information (SKI) Canada…

McKenzie, Grant; Zhang, Hongyu

Platial k-anonymity: Improving location anonymity through temporal popularity signatures 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 = {2023-09-16},

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}

}

Romm, Daniel; Verma, Priyanka; Karpinski, Elizabeth; Sanders, Tracy; McKenzie, Grant

Differences in First-Mile and Last-Mile Behaviour in Candidate Multi-Modal Boston Bike-share Micromobility Trips Journal Article

In: Journal of Transport Geography, vol. 102, iss. June, 2022.

Links | BibTeX

McKenzie, Grant; Romm, Daniel; Zhang, Hongyu; Brunila, Mikael

PrivyTo: A privacy preserving location sharing platform Journal Article

In: Transactions in GIS, 2022.

Abstract | Links | BibTeX

Zhang, Hongyu; McKenzie, Grant

Rehumanize geoprivacy: From disclosure control to human perception Journal Article

In: GeoJournal, 2022.

Abstract | Links | BibTeX