An official website of the United States government
Geo-obfuscation serves as a location privacy protection mechanism (LPPM), enabling mobile users to share obfuscated locations with servers, rather than their exact locations. This method can protect users’ location privacy when data breaches occur on the server side since the obfuscation process is irreversible. To reduce the utility loss caused by data obfuscation, linear programming (LP) is widely employed, which, however, might suffer from a polynomial explosion of decision variables, rendering it impractical in largescale geo-obfuscation applications. In this paper, we propose a new LPPM, called Locally Relevant Geo-obfuscation (LR-Geo), to optimize geo-obfuscation using LP in a time-efficient manner. This is achieved by confining the geoobfuscation calculation for each user exclusively to the locally relevant (LR) locations to the user’s actual location. Given the potential risk of LR locations disclosing a user’s actual whereabouts, we enable users to compute the LP coefficients locally and upload them only to the server, rather than the LR locations. The server then solves the LP problem based on the received coefficients. Furthermore, we refine the LP framework by incorporating an exponential obfuscation mechanism to guarantee the indistinguishability of obfuscation distribution across multiple users. Based on the constraint structure of the LP formulation, we apply Benders’ decomposition to further enhance computational efficiency. Our theoretical analysis confirms that, despite the geo-obfuscation being calculated independently for each user, it still meets geo-indistinguishability constraints across multiple users with high probability. Finally, the experimental results based on a real-world dataset demonstrate that LR-Geo outperforms existing geo-obfuscation methods in computational time, data utility, and privacy preservation.
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Cracking the Code of Geo-Identifiers: Harnessing Data-Based Decision-Making for the Public Good
The accessibility of official statistics to non-expert users could be aided by employing natural language processing and deep learning models to dataset lexicons. Specifically, the semantic structure of FIPS codes would offer a relatively standardized data dictionary of column names and string variable structure to identify: two-digits for states, followed by three-digits for counties. The technical, methodological contribution of this paper is a bibliometric analysis of scientific publications based on FIPS code analysis indicated that between 27,954 and 1,970,000 publications attend to this geo-identifier. Within a single dataset reporting national representative and longitudinal survey data, 141 publications utilize FIPS data. The high incidence shows the research impact. Yet, the low proportion of only 2.0 percent of all publications utilizing this dataset also shows a gap even among expert users. A data use case drawn from public health data implies that cracking the code of geo-identifiers could advance access by helping everyday users formulate data inquiries within intuitive language.
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- Award ID(s):
- 1934942
- PAR ID:
- 10357276
- Editor(s):
- Domenech, Josep; Vicente, María Rosalía
- Date Published:
- Journal Name:
- International Conference on Advanced Research Methods and Analytics
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.4995/CARMA2022.2022.15100
