Toward Lightweight and Privacy-Preserving Data Provision in Digital Forensics for Driverless Taxi
Yanwei Gong, Xiaolin Chang, Jelena Mišić, Vojislav B. Mišić, Junchao Fan, Kaiwen Wang · IEEE Internet of Things Journal · 2025
Data provision, referring to data upload and data access, is one key phase in vehicular digital forensics. The unique features of driverless taxi (DT) bring new issues to this phase: I1) efficient verification of data integrity when diverse data providers (DPs) upload data; I2) DP privacy preservation during data upload; and I3) privacy preservation of both data and investigator (IN) under complex data ownership when accessing data. Considering that the existing works on digital forensics cannot address all these issues, we first propose a novel lightweight and privacy-preserving data provision (LPDP) approach consisting of three mechanisms: 1) privacy-friendly batch verification mechanism (PBVm); 2) data access control mechanism (DACm); and 3) decentralized IN warrant issuance mechanism (DIWIm). PBVm ensures scalable verification of data integrity to address I1. PBVm also ensures the DP privacy preservation in terms of the location privacy and unlinkability of data upload requests to address I2. Besides, DACm and DIWIm are combined to ensure data privacy preservation and the identity privacy of IN in terms of the anonymity and unlinkability of data access requests without sacrificing the traceability to address I3. Security analysis and performance evaluations validate LPDP’s capabilities in addressing the three issues.