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I know where you live: Inferring details of people's lives by visualizing publicly shared location data

Research output: Chapter in Book/Report/Conference proceedingConference paper

Ilaria Liccardi, Alfie Abdul-Rahman, Min Chen

Original languageEnglish
Title of host publicationProceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI)
PublisherACM Digital Library
Pages1-12
Number of pages12
DOIs
Publication statusPublished - 1 May 2016

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Abstract

This research measures human performance in inferring the functional types (i.e., home, work, leisure and transport) of locations in geo-location data using different visual representations of the data (textual, static and animated visualizations) along with different amounts of data (1, 3 or 5 day(s)).

We first collected real life geo-location data from tweets. We then asked the data owners to tag their location points, resulting in ground truth data. Using this dataset we conducted an empirical study involving 45 participants to analyze how accurately they could infer the functional location of the original data owners under different conditions, i.e., three data representations, three data densities and four location types.

The study results indicate that while visual techniques perform better than textual ones, the functional locations of human activities can be inferred with a relatively high accuracy even using only textual representations and a low density of location points. Workplace was more easily inferred than home while transport was the functional location with the highest accuracy. Our results also showed that it was easier to infer functional locations from data exhibiting more stable and consistent mobility patterns, which are thus more vulnerable to privacy disclosures.

We discuss the implications of our findings in the context of privacy preservation and provide guidelines to users and companies to help preserve and safeguard people’s privacy.

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