An Evaluation Framework for Assessing the Impact of Location Privacy on Geospatial Analysis

Abstract

The need for addressing geoprivacy in location based services has increased the offer of mechanisms that protect location information, however, these algorithms are not always developed to ensure the usability of the data and therefore, their adoption is not wide. In this work, a framework is presented to evaluate the effects of geoprivacy mechanisms on the quality of geodata to provide insights into how the data is affected for geospatial analysis. For this purpose, a toolkit of indices was developed to evaluate different characteristics of the data before and after a geoprivacy mechanism is implemented, providing a criterion to select one of them. The indices measure the changes in the presence of clusters through the quantification of hotspots in hotspot analysis and the difference observed in heatmaps of the concentration of the geodata. Variations in global indices like the Nearest Neighbor Index (NNI) and the orientation of the standard deviational ellipse are also measured. For demonstration, the data of crime arrests in New York was used for the month of January in 2017 and 2018. Five mechanisms were tested with different settings, resulting with the NRand-K algorithm producing fewer alterations to the reference data, preserving its initial characteristics better than the other mechanisms.

A comparison of obfuscation methods used for privacy protection: Exploring the challenges of polygon data in agricultural research

“…These results are consistent with those obtained by Zurbaran et al. (2020), who applied the Pinwheel method with 30° and 100° on crime data. However, just based on the comparison of two angles, a definite conclusion may not be accurate.…”

“…These results are in agreement with the findings of Zurbaran et al. (2020), who showed that the density‐based NRand‐ k method outperformed a handful of other methods in terms of spatial pattern preservation, followed by the Pinwheel and Donut methods applied on crime data. Similarly, Zurbarán et al.…”

“…The locations can essentially reveal the identity of individuals' information, which might contain personal and sensitive data. To reduce the risk of identification and to protect data privacy for point‐based data, several obfuscation methods have been developed (Armstrong et al, 1999; Kwan, Casas, & Schmitz, 2004; Montazeri, Houmansadr, & Pishro‐Nik, 2016; Seidl, Jankowski, & Clarke, 2018; Wang & Kwan, 2020; Zurbaran, Salazar, Brovelli, & Wightman, 2020). Anonymization and randomization are two types of point‐based techniqes that are used widely to protect geoprivacy while preserving statistical analysis.…”

“…To reduce the risk of identification and to protect data privacy for point-based data, several obfuscation methods have been developed (Armstrong et al, 1999;Kwan, Casas, & Schmitz, 2004;Montazeri, Houmansadr, & Pishro-Nik, 2016;Seidl, Jankowski, & Clarke, 2018;Wang & Kwan, 2020;Zurbaran, Salazar, Brovelli, & Wightman, 2020).…”

A comprehensive review on the users’ identity privacy for 5G networks

“…[88,98] Used encryption to hideTMSI ■TMSI is hiding ■Auth. message size did not change ■Communication overhead is high ■Computation complexity is high ■It contains the TMSI if the attack gets it [1, 84, 97, 98] Used either expired time, encryption C-RNTI or LBE in LBS ■The location is encrypted ■Communication overhead is high ■Computation complexity is high ■C-RNTI.…”