Proving the value potential of MPC for MaaS

Proving the value potential of MPC for MaaS

This use case was initiated in collaboration with the Data Sharing Coalition, one of the founding partners of the CoE-DSC.

Mobility as a Service (MaaS) is a hot topic to innovate the mobility sector. To realise the MaaS ecosystem, data sharing is key. However, due to barriers regarding commercial sensitivity and privacy of data, mobility providers are hesitant to share their data. Multi-party computation (MPC) can help reduce these data sharing barriers because sensitive source data is not revealed. In this use case, our participants Publiek Vervoer and Roseman Labs, in collaboration with the Data Sharing Coalition, explore the value potential of the use of MPC to realise MaaS.

Why MaaS is crucial for mobility innovation

MaaS is the integration of all kinds of various transportation modes (e.g., public transport, shared mobility) to enable tailored door-to-door travels in a single customer experience. As such, MaaS has the potential to improve the coordination of mobility supply and demand and provide travellers with convenient alternatives to car ownership. To optimally balance supply and demand in the ecosystem, an integral view on travel patterns and customer behaviour is required. To achieve this, mobility providers must make data available and portable (e.g. transactions, account data, planning) to other mobility sector players.

Barriers to data sharing in the mobility sector

In general, mobility providers are hesitant to share their data as they consider data a key asset for their business model and competition with other mobility providers. For example, data about preferences and travel behaviour of travellers. This information is valuable for providers competing for travellers or for winning contracts for tenders. Additionally, the privacy sensitivity of data is a concern. Much relevant data contains personally identifiable information (PII) and sharing this data is limited by the GDPR.

How multi-party computation (MPC) helps reduce barriers to data sharing

MPC can help reduce data sharing barriers by not revealing sensitive source data. MPC allows different organisations to create insights from data from various sources without any individual organisation revealing their source data to other organisations. This is done by encrypting and fragmenting source data before and during calculations to generate insights across organisations in a secure manner. By encrypting and fragmenting data, the data itself is not shared, meaning each individual organisations’ data remains hidden from other organisations involved.

MPC-based data collaboration should reduce commercial barriers as source data is not transferred and will not be revealed in computations. This prevents potential competitors from benefiting from the use of other organisations’ data for their own commercial purposes. MPC-based data collaboration also reduces the legal barriers to data sharing as no PII is shared. However, organisations still need to process PII. Therefore, monitoring the legal context and impact of multi-party calculations remains critical as it is still in development.

Testing the value potential of MPC for MaaS in a pilot

The value potential of MPC will be tested in a MaaS-pilot conducted in the North of the Netherlands (region Groningen-Drenthe). For this pilot, a specific segment of travellers that rely on the Social Support Act (in Dutch: WMO), are offered free public transport to stimulate a shift in their mobility habits: from more expensive personalised offerings to affordable and convenient MaaS-options. The pilot is currently ongoing but at this point, no detailed analyses can be performed to support the hypotheses and gain insights into the impact of the pilot due to privacy barriers to data collaboration.

MPC enables detailed insights into the results of the pilot that will be obtained by combining data sources from various actors such as authorities, transaction processors and municipalities. These insights could answer questions such as ‘Does free or discount on public transport lead to a reduction in the municipality’s total travel costs while if the number of travel kilometres is stable or increasing?’, ‘For which age group is free/discounted public transport most effective?’ or ‘Is the effect of this free/discount on public transport greater in urban or rural areas?’

Enabling scalability to facilitate a larger MPC for MaaS ecosystem

Based on outcomes of the pilot, the Data Sharing Coalition will support participants with strategy development to scale up the use case. A roadmap to scale up to more organisations and use cases will be created together with a generic interaction model as the basis for a larger MPC for MaaS ecosystem. Also, the results of this use case can possibly serve as a basis for multiple use cases and further scaling up in other data sharing initiatives.

Read more about another one of the Data Sharing Coalition’s use cases in which MPC plays a key role to improve the monitoring of human trafficking. This use case served as an inspiration for this use case.

Do you want to know more about this use case? Or do you have an interesting idea to define and realise new cross-sectoral use cases of data sharing? Please send us an email at


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