PAMDI: Privacy aware missing data inference scheme for sparse mobile crowd sensing

The ubiquity of mobile devices has birthed one of the most promising IoT applications called Mobile Crowd Sensing (MCS) wherein mobile devices carried around by a crowd are used to sense phenomena of interest. Subsequently, sensed data are collected, aggregated and analysed to extract useful information. Sparse Mobile Crowd Sensing (SMCS) aims at reducing the sensing overhead (e.g., battery consumption, incentive cost, etc.) by lowering the number of sensing tasks performed. Sensed data thus collected are used to infer missing values. However, it must be ensured that user’s private information (e.g., user’s home location) cannot be derived from the sensed data shared by a user. We propose a novel approach entitled ‘Privacy Aware Missing Data Inference Scheme for Sparse Mobile Crowd Sensing (PAMDI)’ which employs the concept of perceptual hash for ensuring privacy while trying to maintain performance guarantees. Simulation results with the help of two real-world data-sets point towards the feasibility of the proposed approach for provisioning user privacy. We use regression algorithms for missing data inference in PAMDI and find that linear regression algorithms work best with the proposed privacy approach as compared to non-linear regression algorithms. Moreover, we observe that inference accuracy is more or less maintained even after introducing privacy with the proposed approach. In particular, for the first data-set (Temperature data-set), the mean absolute error (MAE) and root mean squared error (RMSE) values obtained by the linear algorithms using the proposed approach are about 2.65 ∘ C and 2 . 9 ∘ C respectively. On the other hand, the corresponding MAE and RMSE values generated by the linear algorithms when no privacy is introduced are about 2.25 ∘ C and 2.85 ∘ C respectively. For non-linear algorithms, the corresponding error values are higher. We also observe the same trend in the results of the second data-set.