# Privacy-Aware Identity Cloning Detection based on Deep Forest

arXiv: https://arxiv.org/abs/2110.10897 · HTML: https://mlsanity.com/paper/2110.10897/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 21 Oct 2021.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Social-Sensor Composition for Tapestry Scenes](https://mlsanity.com/paper/2003.13684/) (2020)
- [NPS-AntiClone: Identity Cloning Detection based on Non-Privacy-Sensitive User Profile Data](https://mlsanity.com/paper/2109.15179/) (2021)
- [Social Media Identity Deception Detection: A Survey](https://mlsanity.com/paper/2103.04673/) (2021)

## Built on by (most cited first, 2 of 2)

- [Cloned Identity Detection in Social-Sensor Clouds based on Incomplete Profiles](https://mlsanity.com/paper/2411.01329/) (2024)
- [Social-Sensor Identity Cloning Detection Using Weakly Supervised Deep Forest and Cryptographic Authentication](https://mlsanity.com/paper/2508.09665/) (2025)

## Abstract (arXiv)

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.