GAN-Driven Personalized Spatial-Temporal Private Data Sharing in Cyber-Physical Social Systems

Abstract

The cyber-physical social system (CPSS) enables human social interaction from cyberspace to the physical world by sharing an increasing volume of spatial-temporal data. The sensitive information in the shared data is appealing to the adversaries and various attacks. However, most existing research assumes that the privacy protection level is identical regardless of various requirements, which is not practical. Subsequently, this results in either over-protection or failing to resist the leading attacks like collusion attacks. Motivated by this, a personalized model is proposed by using generative adversarial nets (GAN) to achieve differential privacy and thereby enhancing spatial-temporal private data sharing. A Differential Privacy Identifier is added to the classic GAN with a Generator and a Discriminator . The deployment of the differentially Private GAN (P-GAN) enables the generation of the sanitized data that can perfectly approximate the spatial-temporal trajectory while providing high-level privacy protection. P-GAN optimizes the trade-off between personalized privacy protection and improved data utility while maintaining fast convergence. Simultaneously, the random noise generation guaranteed by GAN breaks the correlation of injected noises and make P-GAN attack-proof against the collusion attacks. Extensive evaluation results on real-world datasets show the superiority of P-GAN from the aspects of optimized trade-off and efficiency.