Abstract
The desire to share one’s location with friends and family or to use location information for navigation and recommendations services is often overshadowed by the need to preserve privacy. As recent progress in big data analytics, ambient intelligence, and conflation techniques is met with the economy’s growing hunger for data, even formerly negligible digital footprints become revealing of our activities. The majority of established geo-privacy research tries to protect an individual’s location by different masking or perturbation techniques or by suppressing and generalizing an individual’s characteristics to a degree where she cannot be singled out from a crowd. In this work we demonstrate that location privacy may already be compromised before these techniques take effect. More concretely, we discuss how everyday digital footprints such as timestamps, geosocial check-ins, and short social media messages, e.g., tweets, are indicative of the user’s location. We focus particularly on places and highlight how protecting place-based information differs from a purely spatial perspective. The presented research is based on so-called semantic signatures that are mined from millions of geosocial check-ins and enable a probabilistic framework on the level of geographic feature types, here Points Of Interest (POI). While our work is compatible with leading privacy techniques, we take a user-centric perspective and illustrate how privacy-enabled services could guide the users by increasing information entropy.
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Notes
- 1.
Following recent literature, we will use the term platial here for ‘place-based’ (Goodchild 2015).
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- 4.
Venue in this case is the Foursquare-specific term for Point of Interest.
References
Adams B, Janowicz K (2012) On the geo-indicativeness of non-georeferenced text. In: ICWSM, pp 375–378
Almuhimedi H, Schaub F, Sadeh N, Adjerid I, Acquisti A, Gluck J, Cranor LF, Agarwal Y (2015) Your location has been shared 5,398 times!: a field study on mobile app privacy nudging. In: The 33rd annual ACM conference on human factors in computing systems (CHI). ACM, pp 787–796
Armstrong MP, Rushton G, Zimmerman DL (1999) Geographically masking health data to preserve confidentiality. Stati Med 18(5):497–525
Batty M (1974) Spat Entropy. Geogr Anal 6(1):1–31
Beldad A, Kusumadewi MC (2015) Heres my location, for your information: the impact of trust, benefits, and social influence on location sharing application use among indonesian university students. Comput Hum Behav 49:102–110
Benisch M, Kelley PG, Sadeh N, Cranor LF (2011) Capturing location-privacy preferences: quantifying accuracy and user-burden tradeoffs. Pers Ubiquitous Comput 15(7):679–694
Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022
Bohn J, Coroamă V, Langheinrich M, Mattern F, Rohs M (2005) Social, economic, and ethical implications of ambient intelligence and ubiquitous computing. In: Ambient intelligence. Springer, Heidelberg, pp 5–29
Clarke CC (2015) A multiscale masking method for point geographic data. Int J Geogr Inf Sci 30(2):1–16
de Montjoye YA, Hidalgo CA, Verleysen M, Blondel VD (2013) Unique in the crowd: the privacy bounds of human mobility. Sci Rep 3
Dixon PM (2002) Ripley’s K function. In: Encyclopedia of environmetrics
Duckham M, Kulik L (2006) Location privacy and location-aware computing. Dyn Mob GIS: Investigating Change Space Time 3:35–51
Dwork C (2011) Differential privacy. In: Encyclopedia of cryptography and security. Springer, pp 338–340
Gambs S, Killijian MO, del Prado Cortez MN (2010) Show me how you move and i will tell you who you are. In: Proceedings of the 3rd ACM SIGSPATIAL international workshop on security and privacy in GIS and LBS. ACM, pp 34–41
Goodchild MF (2015) Space, place and health. Ann GIS 21(2):97–100
Hampton KH, Fitch MK, Allshouse WB, Doherty IA, Gesink DC, Leone PA, Serre ML, Miller WC (2010) Mapping health data: improved privacy protection with donut method geomasking. Am J Epidemiol 172(9):1062–1069
Janowicz K (2012) Observation-driven geo-ontology engineering. Trans GIS 16(3):351–374
Kounadi O, Leitner M (2015) Spatial information divergence: using global and local indices to compare geographical masks applied to crime data. Trans GIS 19(5):737–757
Lin J (1991) Divergence measures based on the shannon entropy. IEEE Trans Inf Theory 37(1):145–151
Lindqvist J, Cranshaw J, Wiese J, Hong J, Zimmerman J (2011) I’m the mayor of my house: examining why people use foursquare-a social-driven location sharing application. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 2409–2418
Mahmud J, Nichols J, Drews C (2014) Home location identification of twitter users. ACM Trans Intell Syst Technol (TIST) 5(3):47
McCallum AK (2002) MALLET: a machine learning for language toolkit. http://mallet.cs.umass.edu
McKenzie G, Janowicz K (2014) Coerced geographic information: The not-so-voluntary side of user-generated geo-content. In: Eighth international conference on geographic information science
McKenzie G, Janowicz K (2015) Where is also about time: a location-distortion model to improve reverse geocoding using behavior-driven temporal semantic signatures. Comput Environ Urban Syst 54:1–13
McKenzie G, Janowicz K, Gao S, Yang JA, Hu Y (2015) POI pulse: a multi-granular, semantic signatures-based approach for the interactive visualization of big geosocial data. Cartographica: Int J Geogr Inf Geovis 50(2):71–85
Seidl DE, Jankowski P, Tsou MH (2015) Privacy and spatial pattern preservation in masked GPS trajectory data. Int J Geogr Inf Sci 1–16
Seidl DE, Paulus G, Jankowski P, Regenfelder M (2015) Spatial obfuscation methods for privacy protection of household-level data. Appl Geogr 63:253–263
Shannon CE (1948) A note on the concept of entropy. Bell Syst Tech J 27:379–423
Sweeney L (2002) k-anonymity: a model for protecting privacy. Int J Uncertainty Fuzziness Knowl Based Syst 10(05):557–570
Weber RH (2010) Internet of things-new security and privacy challenges. Comput Law Secur Rev 26(1):23–30
Zandbergen PA (2014) Ensuring confidentiality of geocoded health data: assessing geographic masking strategies for individual-level data. Adv Med pp 1–14
Zhang S, Freundschuh SM, Lenzer K, Zandbergen PA (2015) The location swapping method for geomasking. Cartography Geogr Inf Sci 1–13
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McKenzie, G., Janowicz, K., Seidl, D. (2016). Geo-Privacy Beyond Coordinates. In: Sarjakoski, T., Santos, M., Sarjakoski, L. (eds) Geospatial Data in a Changing World. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-33783-8_10
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