Privacy-Aware Identity Cloning Detection based on Deep Forest

TL;DR

This paper introduces a privacy-aware deep learning method for detecting identity cloning in social-sensor cloud services, utilizing non-sensitive user data to improve accuracy over existing techniques.

Contribution

The paper presents a novel deep forest-based approach that effectively detects identity cloning while preserving user privacy, outperforming current state-of-the-art methods.

Findings

Significantly higher Precision and F1-score compared to existing models.

Effective detection of identity cloning using non-privacy-sensitive data.

Outperforms state-of-the-art techniques on real-world datasets.

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.