Geo-indistinguishable masking: enhancing privacy protection in spatial point mapping

References

• Allshouse, W. B., Fitch, M. K., Hampton, K. H., Gesink, D. C., Doherty, I. A., Leone, P. A., Serre, M. L., & Miller, W. C. (2010). Geomasking sensitive health data and privacy protection: An evaluation using an e911 database. Geocarto International, 25(6), 443–452. https://doi.org/10.1080/10106049.2010.496496
   PubMed Web of Science ®Google Scholar
• Andrés, M. E., Bordenabe, N. E., Chatzikokolakis, K., & Palamidessi, C. (2013). Geo-indistinguishability: Differential privacy for location-based systems. In Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security (pp. 901–914). Association for Computing Machinery.
   Google Scholar
• Armstrong, M. P., & Ruggles, A. J. (1999). Map hacking: On the use of inverse address-matching to discover individual identities from point-mapped information sources. In Geographic Information and Society: An International Conference. University of Minnesota.
   Google Scholar
• Armstrong, M. P., & Ruggles, A. J. (2005). Geographic information technologies and personal privacy. Cartographica: The International Journal for Geographic Information and Geovisualization, 40(4), 63–73. https://doi.org/10.3138/RU65-81R3-0W75-8V21
   Google Scholar
• Armstrong, M. P., Rushton, G., & Zimmerman, D. L. (1999). Geographically masking health data to preserve confidentiality. Statistics in Medicine, 18(5), 497–525.
   PubMed Web of Science ®Google Scholar
• Baddeley, A., Rubak, E., & Turner, R. (2015). Spatial point patterns: Methodology and applications with R. CRC press.
   Google Scholar
• Bakillah, M., Liang, S., Mobasheri, A., Jokar Arsanjani, J., & Zipf, A. (2014). Fine-resolution population mapping using openStreetMap points-of-interest. International Journal of Geographical Information Science, 28(9), 1940–1963. https://doi.org/10.1080/13658816.2014.909045
   Web of Science ®Google Scholar
• Bordenabe, N. E., Chatzikokolakis, K., & Palamidessi, C. (2014). Optimal geo-indistinguishable mechanisms for location privacy. In Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security (pp. 251–262). Association for Computing Machinery.
   Google Scholar
• Boulos, M. N. K., Curtis, A., & AbdelMalik, P. (2009). Musings on privacy issues in health research involving disaggregate geographic data about individuals. International Journal of Health Geographics, 8(1), 1–8. https://doi.org/10.1186/1476-072X-8-46
   PubMedGoogle Scholar
• Brownstein, J. S., Cassa, C. A., Kohane, I. S., & Mandl, K. D. (2005). Reverse geocoding: Concerns about patient confidentiality in the display of geospatial health data. In AMIA Annual Symposium Proceedings (pp. 905). American Medical Informatics Association.
   Google Scholar
• Brownstein, J. S., Cassa, C. A., Kohane, I. S., & Mandl, K. D. (2006). An unsupervised classification method for inferring original case locations from low-resolution disease maps. International Journal of Health Geographics, 5(1), 1–7. https://doi.org/10.1186/1476-072X-5-56
   PubMedGoogle Scholar
• Brownstein, J. S., Cassa, C. A., & Mandl, K. D. (2006). No place to hide—reverse identification of patients from published maps. New England Journal of Medicine, 355(16), 1741–1742. https://doi.org/10.1056/NEJMc061891
   PubMed Web of Science ®Google Scholar
• Cassa, C. A., Grannis, S. J., Overhage, J. M., & Mandl, K. D. (2006). A context-sensitive approach to anonymizing spatial surveillance data: Impact on outbreak detection. Journal of the American Medical Informatics Association, 13(2), 160–165. https://doi.org/10.1197/jamia.M1920
   PubMed Web of Science ®Google Scholar
• Chainey, S., & Ratcliffe, J. (2013). GIS and crime mapping. John Wiley & Sons.
   Google Scholar
• Charleux, L., & Schofield, K. (2020). True spatial k-anonymity: Adaptive areal elimination vs. adaptive areal masking. Cartography and Geographic Information Science, 47(6), 537–549. https://doi.org/10.1080/15230406.2020.1794975
   Web of Science ®Google Scholar
• Chatzikokolakis, K., Palamidessi, C., & Stronati, M. (2015). Location privacy via geo-ndistinguishability. ACM SIGLOG News, 2(3), 46–69. https://doi.org/10.1145/2815493.2815499
   Google Scholar
• Chen, R., Li, L., Ma, Y., Gong, Y., Guo, Y., Ohtsuki, T., & Pan, M. (2022). Constructing mobile crowdsourced COVID-19 vulnerability map with geo-indistinguishability. IEEE Internet of Things Journal, 9(18), 17403–17416. https://doi.org/10.1109/JIOT.2022.3158895
   Web of Science ®Google Scholar
• Clarke, K. C. (2016). A multiscale masking method for point geographic data. International Journal of Geographical Information Science, 30(2), 300–315. https://doi.org/10.1080/13658816.2015.1085540
   Web of Science ®Google Scholar
• Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27. https://doi.org/10.1109/TIT.1967.1053964
   Web of Science ®Google Scholar
• Crampton, J. W. (2003). Cartographic rationality and the politics of geosurveillance and security. Cartography and Geographic Information Science, 30(2), 135–148. https://doi.org/10.1559/152304003100011108
   Google Scholar
• Crampton, J. W. (2007). The biopolitical justification for geosurveillance. Geographical Review, 97(3), 389–403. https://doi.org/10.1111/j.1931-0846.2007.tb00512.x
   Web of Science ®Google Scholar
• Cromley, E. K., & McLafferty, S. L. (2011). GIS and public health. Guilford Press.
   Google Scholar
• Curry, M. R. (1997). The digital individual and the private realm. Annals of the Association of American Geographers, 87(4), 681–699. https://doi.org/10.1111/1467-8306.00073
   Web of Science ®Google Scholar
• Curtis, A., Mills, J. W., & Leitner, M. (2006a). Keeping an eye on privacy issues with geospatial data. Nature, 441(7090), 150–150. https://doi.org/10.1038/441150d
   PubMed Web of Science ®Google Scholar
• Curtis, A., Mills, J. W., & Leitner, M. (2006b). Spatial confidentiality and GIS: Re-engineering mortality locations from published maps about hurricane katrina. International Journal of Health Geographics, 5(1), 1–12. https://doi.org/10.1186/1476-072X-5-44
   PubMedGoogle Scholar
• Dobson, J. E., & Fisher, P. F. (2003). Geoslavery. IEEE Technology and Society Magazine, 22(1), 47–52. https://doi.org/10.1109/MTAS.2003.1188276
   Web of Science ®Google Scholar
• Dong, K., Guo, T., Ye, H., Li, X., & Ling, Z. (2018). On the limitations of existing notions of location privacy. Future Generation Computer Systems, 86, 1513–1522. https://doi.org/10.1016/j.future.2017.05.045
   Web of Science ®Google Scholar
• Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006). Calibrating noise to sensitivity in private data analysis. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3876, 265–284. https://doi.org/10.1007/11681878_14
   Google Scholar
• El Emam, K., & Arbuckle, L. (2013). Anonymizing health data: Case studies and methods to get you started. O’Reilly Media, Inc.
   Google Scholar
• Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (pp. 226–231). AAAI Press.
   Google Scholar
• Fisher, P., & Dobson, J. (2003). Who knows where you are, and who should, in the era of mobile geography? Geography, 88(4), 331–337. http://www.jstor.org/stable/40573887
   Google Scholar
• Ghinita, G., Zhao, K., Papadias, D., & Kalnis, P. (2010). A reciprocal framework for spatial k-anonymity. Information Systems, 35(3), 299–314. https://doi.org/10.1016/j.is.2009.10.001
   Web of Science ®Google Scholar
• Gurobi Optimization, LLC. (2021). Gurobi optimizer reference manual. https://www.gurobi.com
   Google Scholar
• Hampton, K. H., Fitch, M. K., Allshouse, W. B., Doherty, I. A., Gesink, D. C., Leone, P. A., Serre, M. L., & Miller, W. C. (2010). Mapping health data: Improved privacy protection with donut method geomasking. American Journal of Epidemiology, 172(9), 1062–1069. https://doi.org/10.1093/aje/kwq248
   PubMed Web of Science ®Google Scholar
• Hasanzadeh, K., Kajosaari, A., Häggman, D., & Kyttä, M. (2020). A context sensitive approach to anonymizing public participation gis data: From development to the assessment of anonymization effects on data quality. Computers, Environment and Urban Systems, 83, 101513. https://doi.org/10.1016/j.compenvurbsys.2020.101513
   Web of Science ®Google Scholar
• Kounadi, O., Lampoltshammer, T. J., Leitner, M., & Heistracher, T. (2013). Accuracy and privacy aspects in free online reverse geocoding services. Cartography and Geographic Information Science, 40(2), 140–153. https://doi.org/10.1080/15230406.2013.777138
   Web of Science ®Google Scholar
• Kounadi, O., & Leitner, M. (2014). Why does geoprivacy matter? The scientific publication of confidential data presented on maps. Journal of Empirical Research on Human Research Ethics, 9(4), 34–45. https://doi.org/10.1177/1556264614544103
   PubMed Web of Science ®Google Scholar
• Kounadi, O., & Leitner, M. (2016). Adaptive areal elimination (AAE): A transparent way of disclosing protected spatial datasets. Computers, Environment and Urban Systems, 57, 59–67. https://doi.org/10.1016/j.compenvurbsys.2016.01.004
   Web of Science ®Google Scholar
• Kwan, M.-P., Casas, I., & Schmitz, B. (2004). Protection of geoprivacy and accuracy of spatial information: How effective are geographical masks? Cartographica: The International Journal for Geographic Information and Geovisualization, 39(2), 15–28. https://doi.org/10.3138/X204-4223-57MK-8273
   Google Scholar
• Lee, J.-G., & Kang, M. (2015). Geospatial big data: Challenges and opportunities. Big Data Research, 2(2), 74–81. https://doi.org/10.1016/j.bdr.2015.01.003
   Web of Science ®Google Scholar
• Leitner, M., & Curtis, A. (2004). Cartographic guidelines for geographically masking the locations of confidential point data. Cartographic Perspectives, 49(49), 22–39. https://doi.org/10.14714/CP49.439
   Google Scholar
• Leitner, M., & Curtis, A. (2006). A first step towards a framework for presenting the location of confidential point data on maps—results of an empirical perceptual study. International Journal of Geographical Information Science, 20(7), 813–822. https://doi.org/10.1080/13658810600711261
   Web of Science ®Google Scholar
• Lin, Y. (2023). Privacy and Utility of Geographic Data: Revealing, Evaluating, and Mitigating the Externalities of Geographic Privacy Protection [ Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1681120766846778
   Google Scholar
• Lin, Y., & Xiao, N. (2022). Developing synthetic individual-level population datasets: The case of contextualizing maps of privacy-preserving census data. In AutoCarto 2022, The 24th International Research Symposium on Cartography and GIScience.
   Google Scholar
• Lin, Y., & Xiao, N. (2023a). A computational framework for preserving privacy and maintaining utility of geographically aggregated data: A stochastic spatial optimization approach. Annals of the American Association of Geographers, 113(5), 1035–1056. https://doi.org/10.1080/24694452.2023.2178377
   Web of Science ®Google Scholar
• Lin, Y., & Xiao, N. (2023b). Generating small areal synthetic microdata from public aggregated data using an optimization method. The Professional Geographer, 1–11. https://doi.org/10.1080/00330124.2023.2207640
   Web of Science ®Google Scholar
• Lyon, D. (2010). Surveillance, power and everyday life. In P. Kalantzis-Cope & K. Gherab-Martín (Eds.), Emerging digital spaces in contemporary society: Properties of technology (pp. 107–120). Palgrave Macmillan UK.
   Google Scholar
• Machanavajjhala, A., Kifer, D., Abowd, J., Gehrke, J., & Vilhuber, L. (2008). Privacy: Theory meets practice on the map. In 2008 IEEE 24th International Conference on Data Engineering (pp. 277–286). IEEE.
   Google Scholar
• McKenzie, G., Romm, D., Zhang, H., & Brunila, M. (2022). PrivyTo: A privacy-preserving location-sharing platform. Transactions in GIS, 26(4), 1703–1717. https://doi.org/10.1111/tgis.12924
   Web of Science ®Google Scholar
• Polzin, F., & Kounadi, O. (2021). Adaptive voronoi masking: A method to protect confidential discrete spatial data. In K. Janowicz & J. A. Verstegen (Eds.), 11th International Conference on Geographic Information Science (giscience 2021) - part ii (1:1–1:17). Schloss Dagstuhl – Leibniz-Zentrum für Informatik.
   Google Scholar
• Richter, W. (2018). The verified neighbor approach to geoprivacy: An improved method for geographic masking. Journal of Exposure Science & Environmental Epidemiology, 28(2), 109–118. https://doi.org/10.1038/jes.2017.17
   PubMed Web of Science ®Google Scholar
• Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). DBSCAN revisited, revisited: Why and how you should (still) use DBSCAN. ACM Transactions on Database Systems (TODS), 42(3), 1–21. https://doi.org/10.1145/3068335
   Web of Science ®Google Scholar
• Seidl, D. E., Jankowski, P., & Clarke, K. C. (2018). Privacy and false identification risk in geomasking techniques. Geographical Analysis, 50(3), 280–297. https://doi.org/10.1111/gean.12144
   Web of Science ®Google Scholar
• Seidl, D. E., Paulus, G., Jankowski, P., & Regenfelder, M. (2015). Spatial obfuscation methods for privacy protection of household-level data. Applied Geography, 63, 253–263. https://doi.org/10.1016/j.apgeog.2015.07.001
   Web of Science ®Google Scholar
• Shokri, R., Theodorakopoulos, G., Papadimitratos, P., Kazemi, E., & Hubaux, J.-P. (2013). Hiding in the mobile crowd: Locationprivacy through collaboration. IEEE Transactions on Dependable and Secure Computing, 11(3), 266–279.
   Web of Science ®Google Scholar
• Swanlund, D., Schuurman, N., & Brussoni, M. (2020). Maskmy. xyz: An easy-to-use tool for protecting geoprivacy using geographic masks. Transactions in GIS, 24(2), 390–401. https://doi.org/10.1111/tgis.12606
   Web of Science ®Google Scholar
• Swanlund, D., Schuurman, N., Zandbergen, P., & Brussoni, M. (2020). Street masking: A network-based geographic mask for easily protecting geoprivacy. International Journal of Health Geographics, 19(1), 1–11. https://doi.org/10.1186/s12942-020-00219-z
   PubMed Web of Science ®Google Scholar
• Sweeney, L. (2002a). Achieving k-anonymity privacy protection using generalization and suppression. International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems, 10(5), 571–588. https://doi.org/10.1142/S021848850200165X
   Web of Science ®Google Scholar
• Sweeney, L. (2002b). K-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems, 10(5), 557–570. https://doi.org/10.1142/S0218488502001648
   Web of Science ®Google Scholar
• U.S. Census Bureau. (2019). Public use microdata sample (PUMS) accuracy of the data. https://www2.census.gov/programs-surveys/acs/techdocs/pums/accuracy/2019AccuracyPUMS.pdf
   Google Scholar
• U.S. Census Bureau. (2020). Understanding and using American community survey data. https://www.census.gov/content/dam/Census/library/publications/2020/acs/acsgeneral handbook 2020.pdf
   Google Scholar
• U.S. Census Bureau. (2021). Understanding and using the American community survey public use microdata sample files. https://www.census.gov/content/dam/Census/library/publications/2021/acs/acspums handbook 2021.pdf
   Google Scholar
• U.S. Department of Transportation. (2023). National address database. https://www.transportation.gov/gis/national-address-database
   Google Scholar
• Wang, J., Kim, J., & Kwan, M.-P. (2022). An exploratory assessment of the effectiveness of geomasking methods on privacy protection and analytical accuracy for individual-level geospatial data. Cartography and Geographic Information Science, 49(5), 385–406. https://doi.org/10.1080/15230406.2022.2056510
   Web of Science ®Google Scholar
• Xiao, Y., & Xiong, L. (2015). Protecting locations with differential privacy under temporal correlations. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (pp. 1298–1309). Association for Computing Machinery.
   Google Scholar
• Yan, Y., Xu, F., Mahmood, A., Dong, Z., & Sheng, Q. Z. (2022). Perturb and optimize users’ location privacy using geo-indistinguishability and location semantics. Scientific Reports, 12(1), 20445. https://doi.org/10.1038/s41598-022-24893-0
   PubMed Web of Science ®Google Scholar
• Zhang, S., Freundschuh, S. M., Lenzer, K., & Zandbergen, P. A. (2017). The location swapping method for geomasking. Cartography and Geographic Information Science, 44(1), 22–34. https://doi.org/10.1080/15230406.2015.1095655
   Web of Science ®Google Scholar
• Zimmerman, D. L., & Pavlik, C. (2008). Quantifying the effects of mask metadata disclosure and multiple releases on the confidentiality of geographically masked health data. Geographical Analysis, 40(1), 52–76. https://doi.org/10.1111/j.0016-7363.2007.00713.x
   Web of Science ®Google Scholar