  The era of Big Data has brought with it a richer understanding of user
behavior through massive data sets, which can help organizations optimize the
quality of their services. In the context of transportation research, mobility
data can provide Municipal Authorities (MA) with insights on how to operate,
regulate, or improve the transportation network. Mobility data, however, may
contain sensitive information about end users and trade secrets of Mobility
Providers (MP). Due to this data privacy concern, MPs may be reluctant to
contribute their datasets to MA. Using ideas from cryptography, we propose a
distributed computation protocol between a MA and a MP in which MA obtains
insights from mobility data without MP having to reveal its trade secrets or
sensitive data of its users. This is accomplished in two steps: a commitment
step, and a computation step. In the first step, Merkle commitments and
aggregated traffic measurements are used to generate a cryptographic
commitment. In the second step, MP extracts insights from the data and sends
them to MA. Using the commitment and zero-knowledge proofs, MA can certify that
the information received from MP is accurate, without needing to directly
inspect the mobility data. The protocol is strategyproof for both MA and MP in
the sense that they cannot benefit from strategic behavior. The protocol can be
readily extended to the more realistic setting with multiple MPs via secure
multi-party computation.
