# NPS-AntiClone: Identity Cloning Detection based on Non-Privacy-Sensitive User Profile Data

arXiv: https://arxiv.org/abs/2109.15179 · HTML: https://mlsanity.com/paper/2109.15179/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2021.

## Built on by (most cited first, 3 of 3)

- [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)
- [Social-Sensor Identity Cloning Detection Using Weakly Supervised Deep Forest and Cryptographic Authentication](https://mlsanity.com/paper/2508.09665/) (2025)

## Abstract (arXiv)

Social sensing is a paradigm that allows crowdsourcing data from humans and devices. This sensed data (e.g. social network posts) can be hosted in social-sensor clouds (i.e. social networks) and delivered as social-sensor cloud services (SocSen services). These services can be identified by their providers' social network accounts. Attackers intrude social-sensor clouds by cloning SocSen service providers' user profiles to deceive social-sensor cloud users. We propose a novel unsupervised SocSen service provider identity cloning detection approach, NPS-AntiClone, to prevent the detrimental outcomes caused by such identity deception. This approach leverages non-privacy-sensitive user profile data gathered from social networks to perform cloned identity detection. It consists of three main components: 1) a multi-view account representation model, 2) an embedding learning model and 3) a prediction model. The multi-view account representation model forms three different views for a given identity, namely a post view, a network view and a profile attribute view. The embedding learning model learns a single embedding from the generated multi-view representation using Weighted Generalized Canonical Correlation Analysis. Finally, NPS-AntiClone calculates the cosine similarity between two accounts' embedding to predict whether these two accounts contain a cloned account and its victim. We evaluated our proposed approach using a real-world dataset. The results showed that NPS-AntiClone significantly outperforms the existing state-of-the-art identity cloning detection techniques and machine learning approaches.