# Cloned Identity Detection in Social-Sensor Clouds based on Incomplete Profiles

arXiv: https://arxiv.org/abs/2411.01329 · HTML: https://mlsanity.com/paper/2411.01329/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 44 references that are in the map.
- First posted on arXiv 2 Nov 2024.

## Builds on (most cited first, 8 of 8)

- [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)
- [Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks](https://mlsanity.com/paper/1906.00150/) (2019)
- [Classification with Low Rank and Missing Data](https://mlsanity.com/paper/1501.03273/) (2015)
- [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)
- [Privacy-Aware Identity Cloning Detection based on Deep Forest](https://mlsanity.com/paper/2110.10897/) (2021)

## Built on by (most cited first, 1 of 1)

- [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 approach to effectively detect cloned identities of social-sensor cloud service providers (i.e. social media users) in the face of incomplete non-privacy-sensitive profile data. Named ICD-IPD, the proposed approach first extracts account pairs with similar usernames or screen names from a given set of user accounts collected from a social media. It then learns a multi-view representation associated with a given account and extracts two categories of features for every single account. These two categories of features include profile and Weighted Generalised Canonical Correlation Analysis (WGCCA)-based features that may potentially contain missing values. To counter the impact of such missing values, a missing value imputer will next impute the missing values of the aforementioned profile and WGCCA-based features. After that, the proposed approach further extracts two categories of augmented features for each account pair identified previously, namely, 1) similarity and 2) differences-based features. Finally, these features are concatenated and fed into a Light Gradient Boosting Machine classifier to detect identity cloning. We evaluated and compared the proposed approach against the existing state-of-the-art identity cloning approaches and other machine or deep learning models atop a real-world dataset. The experimental results show that the proposed approach outperforms the state-of-the-art approaches and models in terms of Precision, Recall and F1-score.