Verifiable Privacy-Enhanced Rotation Invariant LBP Feature Extraction in Fog Computing

Abstract

Rotation invariant local binary pattern (RI-LBP) features have been applied in diverse scenarios with the advantages of gray-scale and rotation invariance. Secure fog computing has become an emerging paradigm for enterprises or individuals with a huge volume of private data but limited computing power for feature extraction. Prior secure outsourcing protocols based on LBP and RI-LBP simply focus on local data privacy, which can only resist ciphertext-only attack, and also make extracted features exposed to the cloud. This work focuses on how to effectively ensure data confidentiality and feature integrity. We propose a verifiable privacy-enhanced protocol for RI-LBP feature extraction (VRLBP) based on the fog computing paradigm, which mitigates the aforementioned challenges by involving the proposed symmetric cryptographic scheme where local data and extracted features are proven secure against chosen plaintext attack. Meanwhile, the stage of verification can check the correctness of outsourced features with an overwhelming probability and constant computational complexity. The security analysis and computational costs demonstrate that VRLBP can reduce the computation overhead to around 30% of original feature extraction in a privacy-preserving manner. To exhibit the practical utility, VRLBP is implemented for deepfake detection on five public datasets. Extensive evaluations indicate that VRLBP achieves almost the same accuracy as the original RI-LBP algorithm and outperforms the state-of-the-art protocols.