# Social-Sensor Identity Cloning Detection Using Weakly Supervised Deep Forest and Cryptographic Authentication

arXiv: https://arxiv.org/abs/2508.09665 · HTML: https://mlsanity.com/paper/2508.09665/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 51 references that are in the map.
- First posted on arXiv 13 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [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)
- [A Survey on Multi-view Learning](https://mlsanity.com/paper/1304.5634/) (2013)
- [Social-Sensor Composition for Tapestry Scenes](https://mlsanity.com/paper/2003.13684/) (2020)
- [Conflict Detection in IoT-based Smart Homes](https://mlsanity.com/paper/2107.13179/) (2021)
- [NPS-AntiClone: Identity Cloning Detection based on Non-Privacy-Sensitive User Profile Data](https://mlsanity.com/paper/2109.15179/) (2021)
- [Privacy-Aware Identity Cloning Detection based on Deep Forest](https://mlsanity.com/paper/2110.10897/) (2021)
- [Cloned Identity Detection in Social-Sensor Clouds based on Incomplete Profiles](https://mlsanity.com/paper/2411.01329/) (2024)

## Abstract (arXiv)

Recent years have witnessed a rising trend in social-sensor cloud identity cloning incidents. However, existing approaches suffer from unsatisfactory performance, a lack of solutions for detecting duplicated accounts, and a lack of large-scale evaluations on real-world datasets. We introduce a novel method for detecting identity cloning in social-sensor cloud service providers. Our proposed technique consists of two primary components: 1) a similar identity detection method and 2) a cryptography-based authentication protocol. Initially, we developed a weakly supervised deep forest model to identify similar identities using non-privacy-sensitive user profile features provided by the service. Subsequently, we designed a cryptography-based authentication protocol to verify whether similar identities were generated by the same provider. Our extensive experiments on a large real-world dataset demonstrate the feasibility and superior performance of our technique compared to current state-of-the-art identity clone detection methods.