Prioritizing Privacy When Using Location in Apps is a short article that discusses five specific recommendations for preserving privacy when dealing with location data in general. The recommendations are applicable to shared mobility trip data.

The article 1 discusses five specific recommendations for any use of location data:

  1. De-identification and anonymization. Specific suggestions are to remove any obvious identifiers (vehicle ID may be a linkable identifier in MDS), break trip data down into shorter segments (useful for traffic data, but likely not a viable strategy for many applications of shared mobility data), and discarding the origins and destination end points for trips (again, not a viable strategy for many of the use cases for shared mobility data).
  2. Fuzzing and aggregation. Aggregation groups individual trips with some similarity together into larger groups of trips. Fuzzing can shift trip origins or destinations (perhaps by simply truncating the latitude and longitude data), while still maintaining the level of fidelity needed for a specific use case. Both practices are relevant to shared mobility data and are being used today.
  3. Encryption of data, both at rest and in transit. Location data should be routinely encrypted, and the process should use widely adopted and vetted libraries. While not discussed in this article, New York taxi data that had been released under a freedom of information request was deanonymized because a poorly chosen hashing algorithm was used to encrypt the medallion IDs 2
  4. Access Control. Data access should be limited to those who need it, and procedures put in place for onboarding and offboarding staff who require access.
  5. Providing User Choice. This recommendation is partially relevant for shared mobility. It is likely that neither individual users nor service providers will be given a choice about providing data to the public agency, however agencies should make clear and transparent what data will be collected and how it will be used. 

Another resource, Dockless Open Data 3 from the City of Louisville goes into detail on how they fuzz and aggregate the shared mobility data they collect, down to the level of code examples.