This standard proposes a privacy-preserving computation framework for intelligent transportation. The standard specifies technical requirements for enabling secure data sharing, computation, and collaboration across vehicles, roadside infrastructure, and cloud platforms. The standard provides technical guidance for stakeholders involved in the design, deployment, and application of privacy-preserving computation in intelligent transportation. It also serves as a reference for regulatory agencies in helping enable data security, privacy protection, and compliance. The framework supports applications such as traffic optimization, accident investigation, multi-party data collaboration, and secure decision-making for intelligent mobility services. By integrating secure multi-party computation (MPC), federated learning, zero-knowledge proofs, and trusted execution environments (TEE), the framework enables real-time, privacy-protected decision-making in traffic management, accident liability determination, and intelligent mobility services. All computation processes and data interactions are verifiable and traceable, providing a regulatory basis for compliance auditing and ensuring accountability in intelligent transportation applications.
- Standard Committee
- CTS/DFESC - Digital Finance and Economy Standards Committee
- Status
- Active PAR
- PAR Approval
- 2025-09-10
Working Group Details
- Society
- IEEE Consumer Technology Society
- Standard Committee
- CTS/DFESC - Digital Finance and Economy Standards Committee
- Working Group
-
VRCPCWG - Technical requirements of privacy-preserving computation for vehicle-road-cloud collaboration in intelligent transportation
- IEEE Program Manager
- Soo Kim
Contact Soo Kim - Working Group Chair
- Xu Xiaojie
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