Privacy-Aware Identity Cloning Detection based on Deep Forest

• URL: http://arxiv.org/abs/2110.10897v1
• Date: Thu, 21 Oct 2021 04:55:52 GMT
• Title: Privacy-Aware Identity Cloning Detection based on Deep Forest
• Authors: Ahmed Alharbi, Hai Dong, Xun Yi, Prabath Abeysekara
• Abstract summary: 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.
• Score: 9.051524543426451
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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