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Sanitization of Transportation Data: Policy Implications and Gaps

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  • Bishop, Matt

Abstract

Data about mobility provides information to improve city planning, identify traffic patterns, detect traffic jams, and route vehicles around them. This data often contains proprietary and personal information that companies and individuals do not wish others to know, for competitive and personal reasons. This sets up a paradox: the data needs to be analyzed, but it cannot be without revealing information that must be kept secret. A solution is to sanitize the data—i.e., remove or suppress the sensitive information. The goal of sanitization is to protect sensitive information while enabling analyses of the data that will produce the same results as analyses of the unsanitized data. However, protecting information requires that sanitized data cannot be linked to data from other sources in a manner that leads to desanitization. This project reviews typical strategies used to sanitize datasets, the research on how some of these strategies are unsuccessful, and the questions that must be addressed to better understand the risks of desanitization.

Suggested Citation

  • Bishop, Matt, 2021. "Sanitization of Transportation Data: Policy Implications and Gaps," Institute of Transportation Studies, Working Paper Series qt6ct4b3g9, Institute of Transportation Studies, UC Davis.
    • Handle: RePEc:cdl:itsdav:qt6ct4b3g9
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    1. Kitamura, Ryuichi & Fujii, Satoshi & Pas, Eric I., 1997. "Time-use data, analysis and modeling: toward the next generation of transportation planning methodologies," Transport Policy, Elsevier, vol. 4(4), pages 225-235, October.
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    Keywords

    Engineering; Data; traffic data; data sharing; data cleaning; data fusion; data privacy; computer security; transportation planning;
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