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As location data have been increasingly adopted in location-based advertising (LBA), revealing locations to untrusted service providers has raised severe privacy concerns. Recent studies propose obfuscation mechanisms built upon geo-indistinguishability (geo-IND) to provide formal privacy guarantee. Unfortunately, due to the high degree of spatiotemporal regularity in human mobility pattern, the privacy cost will be unacceptably high in this situation, leading to accurate inference of user real locations. In this study, we identify this privacy risk in LBA scenarios under long-term and multi-platform assumption. We demonstrate an attacker can infer $75\%\sim 90\%$75%∼90% of top-1 locations within a range of only 200 meters. To address it, we propose PrivLocAd, a novel system which can provide longitudinal privacy guarantee. The novelty of PrivLocAd stems from a novel surrogate-based obfuscation, which generates multiple surrogate locations to improve the privacy-utility trade-off. In addition, two novel obfuscation mechanisms, the two-stage Gaussian and multi-level surrogate generation mechanism in charge of surrogate generation can achieve the longitudinal privacy guarantee in intra- and inter-platform condition respectively. Our experimental results demonstrate PrivLocAd is able to defend against the attack, which reduces the inference rate to less than 1% of user top-1 locations in the 200 m range.

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.