Paper Info
Title
I Know Where You Live: Inferring Details of People's Lives by Visualizing Publicly Shared Location Data.
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.
Year
DOI
Venue
2016
10.1145/2858036.2858272
CHI
Keywords
Field
DocType
Location data, data representations, empirical study, privacy, K.4.1 Computers and Society: Privacy
World Wide Web,Information retrieval,Computer science,Location data,Ground truth,Human–computer interaction,Empirical research,Low density
Conference
ISBN
Citations 
PageRank 
978-1-4503-3362-7
5
0.39
References 
Authors
20
3
Name
Order
Citations
PageRank
Ilaria Liccardi114512.33
Alfie Abdul-Rahman2181.65
Min Chen3129382.69