A privacy-enhanced human activity recognition using GAN & entropy ranking of microaggregated data

The convergence of Internet of Things (IoT) and edge computing allows for the collection and sharing of data from human wearable devices. With the huge amount of data transferred among those devices, privacy lies at the forefront of the concerns that must be addressed while preserving the usefulness of the shared data. This research proposes a microAggregation-generative based privacy-preserving model for human activity recognition by analyzing IoT data. Although generative deep neural networks have been widely used for data perturbation and privacy-preserving models, data leakage and disclosing private information of the training samples through linkage attacks remain as major threats when employing traditional anonymization approaches. In addition, noisy records pose a threat to both privacy and quality of the resulting anonymized data. To address these challenges, we propose a novel approach to perturb IoT data using Generative Adversarial Networks (GAN) and microAggregation while preserving both data privacy and utility. Our approach reduces the size of the original dataset by employing an entropy-preserving measure to discard outlier records when data is microaggregated. The performance of the proposed approach was measured using several criteria such as Classification Accuracy, Precision, Recall, and F-score to compare before and after anonymization. As a result, the proposed GAN-MicroAggregation privacy-preserving technique showed a remarkable performance in terms of preserving accuracy after anonymization. Moreover, the privacy of the anonymized data was measured, showing the benefits of the proposed approach when sharing IoT datasets with minimal data inference attack surface.