2016
DOI: 10.5194/isprs-archives-xli-b2-535-2016
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The Trade-Off Between Privacy and Geographic Data Resolution. A Case of GPS Trajectories Combined With the Social Survey Results

Abstract: ABSTRACT:Trajectory datasets are being generated in great volumes due to high levels of Global Positioning System (GPS) and Location-Based Services (LBS) use. Such data are increasingly being collected for a variety of academic, industrial and recreational reasons, sometimes together with other strands of personal data such as socio-demographic, social survey and other sensor data carried/worn by the person. In such cases, not only are movement data of a person available but also data on potentially a wide var… Show more

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Cited by 4 publications
(5 citation statements)
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“…Worryingly, not all smartphone applications intended for neuropsychiatric conditions seem to have a privacy policy, and if this is available at all, it is often inappropriately complex for lay people [93]. It is possible, however, to effectively anonymize GPS data in order to prevent re-identification use of ancillary data [94]. Given these concerns, it is important to inform participants about what happens to their data in an accessible, transparent way.…”
Section: Practical Considerations For Passive Assessmentmentioning
confidence: 99%
“…Worryingly, not all smartphone applications intended for neuropsychiatric conditions seem to have a privacy policy, and if this is available at all, it is often inappropriately complex for lay people [93]. It is possible, however, to effectively anonymize GPS data in order to prevent re-identification use of ancillary data [94]. Given these concerns, it is important to inform participants about what happens to their data in an accessible, transparent way.…”
Section: Practical Considerations For Passive Assessmentmentioning
confidence: 99%
“…However, the main problem in the scientific community is to share this data publicly due to people's privacy [21] , so it is essential to anonymise it to share data for further research. The anonymisation technique must enable data access while maintaining people's privacy and keeping the data structure to analyse it efficiently within the original research purpose [ 22 , 23 ] despite the undeniable semantic information loss [24] . Empirical APL data collected on a longitudinal basis are rarely publicly available, mainly because of the costs and difficulty of acquiring data over a long period of time [25] .…”
Section: Data Descriptionmentioning
confidence: 99%
“…Most scientific fields now routinely use Big Data thanks to advances in sensors, data loggers, data storage, data sharing platforms, and the accompanying proliferation of data-mining methods. Additionally, data fuzzing and anonymization techniques have become a major research area because the issue of data privacy has become an essential subject with the advent of these networks of sensors and data loggers that collect spatial and temporal data at a higher resolution than ever before (Cormode & Srivastava, 2009;Ghinita et al, 2007;Sila-Nowicka & Thakuriah, 2016).…”
Section: Access To Advanced and Custom Data Fuzzing And Anonymization...mentioning
confidence: 99%
“…Even though data fuzzing and anonymization compromises information that can be gleaned for research, over the years advances in data fuzzing methodologies has minimized information loss. For example, in the case of location data fuzzing in geospatial datasets rather than randomly moving data points, additional environmental layers that have relevance to the measured variable are used to relocate data so that location is consistent according to some environmental variables meaningful for the target research question without compromising privacy (Sila-Nowicka & Thakuriah, 2016). While these techniques are readily available for data scientists, it is not common for data-sharing platforms to include these analytical methods so that they are readily available for use.…”
Section: Access To Advanced and Custom Data Fuzzing And Anonymization...mentioning
confidence: 99%