A Geoprivacy by Design Guideline for Research Campaigns That Use Participatory Sensing Data

  title={A Geoprivacy by Design Guideline for Research Campaigns That Use Participatory Sensing Data},
  author={Ourania Kounadi and Bernd Resch},
  journal={Journal of Empirical Research on Human Research Ethics},
  pages={203 - 222}
  • O. Kounadi, Bernd Resch
  • Published 23 April 2018
  • Computer Science
  • Journal of Empirical Research on Human Research Ethics
Participatory sensing applications collect personal data of monitored subjects along with their spatial or spatiotemporal stamps. The attributes of a monitored subject can be private, sensitive, or confidential information. Also, the spatial or spatiotemporal attributes are prone to inferential disclosure of private information. Although there is extensive problem-oriented literature on geoinformation disclosure, our work provides a clear guideline with practical relevance, containing the steps… 

Tables from this paper

Towards geoprivacy guidelines for spatial data
This paper proposes an approach towards practical privacy guidelines for the different stages of a research effort that collects and/or uses “sensitive” spatial data. Specifically, we focus on: a)
Privacy Threats and Protection Recommendations for the Use of Geosocial Network Data in Research
This paper examines recent research efforts on the spectrum of privacy issues related to geosocial network data and identifies the contributions and limitations of these research efforts.
Decentralized geoprivacy: leveraging social trust on the distributed web
A geoprivacy framework that couples two emerging technologies – decentralized data storage and discrete global grid systems – to facilitate fine-grained user control over the ownership of, access to and map-based representation of their data.
Location Privacy in the Wake of the GDPR
A perspective is interdisciplinary: it draws from computer science to describe three scenarios of transformation of volunteered or observed information to inferred information about a natural person and from cultural theory to distinguish four privacy cultures emerging within the EU in the wake of GDPR.
Digital Earth Ethics
This chapter focuses on (geo)privacy, and takes the point of view of Alice, an individual ‘data subject’ encountered in data protection legislation, and suggests ways to account for privacy as a sociocultural phenomenon in the future.
Adaptive Voronoi Masking: A Method to Protect Confidential Discrete Spatial Data
This study develops an alternative approach, referred to as Adaptive Voronoi Masking (AVM), which is based on the concepts of Adaptive Aerial Elimination and Voronoa Masking and outperforms all methods regarding the risk of false re-identification because no data point is moved to a residence.
Evaluating the Representativeness of Socio-Demographic Variables over Time for Geo-Social Media Data
A generic methodology for investigating the representativeness of geo-social media data for population groups of similar statistical predictive power based on reference data, which shows that densely populated areas tend to be underrepresented consistently in non-spatial models.
Commuter Mobility Patterns in Social Media: Correlating Twitter and LODES Data
It is shown that Twitter data can be used to approximate LODES on the county level and on the street segment level, but it also contains information about non-commuting-related regular travel, which shows how factors like rush hour times and weekends impact mobility.
How do people perceive the disclosure risk of maps? Examining the perceived disclosure risk of maps and its implications for geoprivacy protection
ABSTRACT This research examines how people subjectively perceive the disclosure risk of a map using original data collected in an online survey with 856 participants. The results indicate that
Comparing Twitter and LODES Data for Detecting Commuter Mobility Patterns
The research project set out to determine whether GSND may be used to augment US Census LODES data beyond commuting trips and whether it may serve as a short-term substitute for commuting trips found that the reverse is true and the common practice of employing LODes data to extrapolate to overall traffic demand is indeed justified.


No place to hide: a study of privacy concerns due to location sharing on geo-social networks
The results clearly demonstrate users’ needs for improving their knowledge, access and visibility of their data sets as well as for means to control and manage their location data.
ARTSense: Anonymous reputation and trust in participatory sensing
This paper proposes ARTSense, a framework to solve the problem of “trust without identity” in participatory sensing networks, which consists of a privacy-preserving provenance model, a data trust assessment scheme and an anonymous reputation management protocol.
A survey on privacy in mobile participatory sensing applications
Geomasking sensitive health data and privacy protection: an evaluation using an E911 database
This work evaluated the performance of donut geomasking in Orange County, North Carolina and found Census block groups in mixed-use areas with high population distribution heterogeneity were the most likely to have privacy protection below selected criteria.
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
A middleware architecture and algorithms that can be used by a centralized location broker service that adjusts the resolution of location information along spatial or temporal dimensions to meet specified anonymity constraints based on the entities who may be using location services within a given area.
Protection of Geoprivacy and Accuracy of Spatial Information: How Effective Are Geographical Masks?
The need to protect geoprivacy while making georeferenced, individual-level data available in such a way that analytical results are not significantly affected is addressed.
Movement data anonymity through generalization
This position paper briefly presents an approach for the generalization of movement data that can be adopted for obtaining k-anonymity in spatio-temporal datasets and can be used to realize a framework for publishing of spatio/temporal data while preserving privacy.
Preventing Location-Based Identity Inference in Anonymous Spatial Queries
This work proposes transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without revealing the query source.
Why Does Geoprivacy Matter? The Scientific Publication of Confidential Data Presented on Maps
  • O. KounadiM. Leitner
  • Mathematics
    Journal of empirical research on human research ethics : JERHRE
  • 2014
One of the more significant findings of this study is that efforts to instill sensitivity to location privacy and disclosure risk have been relatively unsuccessful.