In the last blog post Andrew showed how to apply privacy-preserving techniques to mobility data. We’re now going to show how these techniques apply in real world contexts. Specifically how transport authorities can use them to meet changing transport needs after COVID-19 and ensure equal access to cycling.

A photo of a Black man riding a bike on a city street. The photo is coloured so the man and the bike are pale green and the background is bright pink,

Authorities must ensure Black people have equal access to cycling or health inequalities will get worse

The Mayor of London listed cycling and walking as key population health indicators in the London Health Inequalities Strategy. The pandemic has only amplified the need for people to use cycling as a safer and healthier mode of transport. Yet as the majority of cyclists are white, Black communities are less likely to get the health benefits that cycling provides. Groups like Transport for London (TfL) should monitor how different communities cycle and who is excluded. Organisations like the London Office of Technology and Innovation (LOTI) could help boroughs procure privacy preserving technology to help their efforts.

But at the moment, it’s difficult for public organisations to access mobility data held by private companies. One reason is because mobility data is sensitive. Even if you remove identifiers like name and address, there’s still a risk you can reidentify someone by linking different data sets together. This means you could track how an individual moved around a city. I wrote more about the privacy risks with mobility data in a previous blog post. The industry’s awareness of privacy issues in using and sharing mobility data is rising. In the case of Los Angeles Department of Transport’s Mobility Data Specification (LADOT), Uber is concerned about sharing anonymised data because of the privacy risk. Both organisations are now involved in a legal battle to see which has the rights to the data. This might have been avoided if Uber had applied privacy preserving techniques.

Monitor mobility provision without compromising privacy

Privacy preserving techniques can help mobility providers share important insights with authorities without compromising peoples’ privacy.

Instead of requiring access to all customer trip data, authorities could ask specific questions like, where are the least popular places to cycle? If mobility providers apply techniques like randomised response, an individual’s identity is obscured by the noise added to the data. This means it’s highly unlikely that someone could be reidentified later on. And because this technique requires authorities to ask very specific questions – for randomised response to work, the answer has to be binary, ie Yes or No – authorities will also be practicing data minimisation by default.

It’s easy to imagine transport authorities like TfL combining privacy preserved mobility data from multiple mobility providers to compare insights and measure service provision. They could cross reference the privacy preserved bike trip data with demographic data in the local area to learn how different communities cycle. The first step to addressing inequality is being able to measure it.

The website of a transport authority. On the left, the title reads 'Black communities do not have equal access to bike share services'. On the right, there's a map of London with two areas highlighted. There's a pink area for the distribution of Black communities and a green area for the distribution of bike share trips. The areas where they overlap are blue and there are two of them: one in South London near the river and other in East London. All the data used to build this map has been privacy-protected.
Prototype showing how privacy-protected data about bike trips could be used to understand the availability of micro-mobility services in areas of the city where Black communities live.

Publish data for public value

Transport authorities are in a unique position to convene data from multiple sources, public and private, and open them for public value. Prioritising public value is an important principle in an society-centered design approach. Transport authorities could make privacy preserved data open to the public, making data sets that would have previously been considered too sensitive, safer to share.

For example:

Prototype showing how authorities could share information about bike share trips and fines to understand how Black communities are disproportionately prosecuted or to learn which roads are less safe.

Giving communities access to data about them can help support local grass-roots initiatives have a greater impact. Imagine this data was available to the Bicycle Mayor’s program, a global network of cycling advocates and leaders that aim to boost cycling in different communities. Or for Lebogang Mokwena, the Bicycle Mayor in Cape Town, runs adult cycle lessons for women of colour. Mokwena might benefit from information about where Black communities cycle less to better target her education events.

6 candid photos of different people in different scenarios posing with their bikes.
Bicycle Mayors from the Americas.

With access to insights from previously closed sensitive data, community groups could better target their efforts, monitor the impact of transport and infrastructure decisions and work with companies to deliver anti-racist products and services.

What’s next?

It’s been brilliant to see other organisations take a stance on inequality in mobility. I’ve been inspired by a series of editorials in Bicycling mag written by Black cycling leaders and activists like Tamika Butler, Nedra Deadwyler and Rachel Olzer. We’re happy to support this work if ever we can!

For everyone who’s reached out to speak about how they can apply privacy preserving techniques to data they hold – thank you! For public and private organisations that either hold or are interested in data sharing, we hope we helped demonstrate practically how you might embed privacy preserving techniques in the agreement. We’ll write about what these techniques make possible for mobility providers in our next and final post.

If you’re interested in testing the randomised response technique or have already tried it, we’d love to hear from you.