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NRand‐K: Minimizing the impact of location obfuscation in spatial analysis
Author(s) -
Zurbarán Mayra,
Wightman Pedro,
Brovelli Maria,
Oxoli Daniele,
Iliffe Mark,
Jimeno Miguel,
Salazar Augusto
Publication year - 2018
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/tgis.12462
Subject(s) - obfuscation , context (archaeology) , masking (illustration) , computer science , location based service , asset (computer security) , computer security , service provider , information privacy , quality (philosophy) , crowdsourcing , data quality , service (business) , internet privacy , data mining , data science , business , world wide web , geography , telecommunications , art , philosophy , archaeology , epistemology , marketing , visual arts
Location privacy, or geoprivacy, is critical to secure users’ privacy in context‐aware applications. Location‐based services pose privacy risks for users, due to the inferences that could be made about them from their location information and the potential misuse of this data by service providers or third‐party companies. A common solution is to apply masking or location obfuscation, which degrades location information quality while keeping a geographic coordinate structure. However, there is a trade‐off between privacy, quality of service, and quality of information, the last one being a valuable asset for companies. NRand is a location privacy mechanism with obfuscation capabilities and resistance against filtering attacks. In order to minimize the impact on location information quality, NRand‐K has been introduced. This algorithm is designed for use when releasing location information to third parties or as open data with privacy concerns. To assess the impact of location obfuscation on exploratory spatial data analysis (ESDA), a comparison is performed between obfuscated data with NRand, NRand‐K, and unaltered data. For the experiments, geolocated tweets collected during the Central Italy 2016 earthquake are used. Results show that NRand‐K reduces the impact on ESDA, where inferences were similar to those obtained with the unaltered data.