Trust but Verify: Cryptographic Data Privacy for Mobility Management
Matthew Tsao, Kaidi Yang, Stephen Zoepf, Marco Pavone

TL;DR
This paper introduces a cryptographic protocol enabling transportation authorities to analyze mobility data for insights without compromising user privacy or revealing trade secrets, using commitments, zero-knowledge proofs, and differential privacy.
Contribution
It presents a novel cryptographic protocol combining commitments and zero-knowledge proofs for privacy-preserving mobility data analysis, including a differentially private variant for large queries.
Findings
Protocol ensures data privacy and integrity.
Verifiable for both authorities and providers.
Extensible to multiple providers with secure multi-party computation.
Abstract
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 an interactive 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…
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Taxonomy
TopicsVehicular Ad Hoc Networks (VANETs) · Cryptography and Data Security · Human Mobility and Location-Based Analysis
