Differential Privacy IoT Streaming Data via Generative Adversarial Networks

A generative adversarial network (GAN) is introduced as an intermediary between the original IoT data and the differential privacy mechanism. The GAN learns the underlying distribution of the original data and generates synthetic data that preserves statistical properties while being decoupled from sensitive individual records. This intermediary allows privacy protection through differential privacy while maintaining data utility through the learned distribution, resolving the contradiction between anonymity and usefulness.

Instead of directly anonymizing original data which loses utility, the system creates a copy of the data distribution through the GAN's learned model. The generator produces synthetic data samples that replicate the statistical characteristics and patterns of the original data without containing actual sensitive information. This copying approach enables both privacy protection and preservation of data utility for analysis.

The GAN model is trained in advance on the IoT data distribution before deployment. This preliminary training phase captures the essential patterns and characteristics of the data. Once trained, the generator can rapidly produce synthetic data samples without requiring complex real-time computations during data generation, thus maintaining data utility while reducing ongoing computational complexity.

The GAN becomes self-sufficient after training, where the generator can autonomously produce high-quality synthetic data samples without requiring continuous access to the original data or complex computational resources. The model serves itself by internally generating realistic data patterns based on its learned representation, reducing the computational burden during operational phases.