A Practical Data Audit Scheme With Retrievability and Indistinguishable Privacy-Preserving for Vehicular Cloud Computing

In vehicular cloud computing (VCC), cloud servers provide enormous storage and powerful computing capacity to Vehicular Ad-hoc Networks (VANETs). Resource-constrained vehicles outsource data to vehicular cloud platforms for timely traffic safety services, e.g., navigation, accident alarms, etc. Auditing the authenticity of data has become a critical issue in outsourcing data to untrusted servers. Existing data audit methods encode all data with error correction codes (ECC) techniques that retrieve corrupted data by downloading all data. The communication overhead of such methods is O(n) (n is the number of data blocks) which is unbearable for vehicles with limited resources. In addition, these schemes employ an inaccurate privacy-preserving model. This will lead to data leakage in the third-party audit process. Although they use randomness to confuse parts of the proof that is used to prove the data state, a small amount of information is still distinguishable. For such, in this paper, we propose a practical data audit scheme with retrievability and indistinguishable privacy-preserving to efficiently audit the state of outsourced data. We improve the Invertible Bloom Filter (IBF) to compress redundancy locally, which can retrieve corrupted data without prior context. Furthermore, we define an indistinguishable privacy-preserving model to capture the complete semantics of repeated audit attacks and achieve indistinguishability in the audit. We prove that our scheme is secure against adaptive chosen message attacks and is indistinguishable privacy-preserving against repeated audit attacks. The experiment results demonstrate that for 1.9 GB data when root n blocks are corrupted, auditors complete a check in 3.31 seconds with 99% confidence, and vehicles retrieve corrupted data in 3.16 seconds with 16.67 MB communication overhead.