Privacy-Aware Identity Cloning Detection based on Deep Forest
This addresses identity deception in social-sensor cloud services, presenting an incremental improvement with specific performance gains.
The paper tackles identity cloning detection for social-sensor cloud services by using non-privacy-sensitive user profile data and a deep learning model, achieving significant improvements in Precision and F1-score over state-of-the-art techniques.
We propose a novel method to detect identity cloning of social-sensor cloud service providers to prevent the detrimental outcomes caused by identity deception. This approach leverages non-privacy-sensitive user profile data gathered from social networks and a powerful deep learning model to perform cloned identity detection. We evaluated the proposed method against the state-of-the-art identity cloning detection techniques and the other popular identity deception detection models atop a real-world dataset. The results show that our method significantly outperforms these techniques/models in terms of Precision and F1-score.