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

• URL: http://arxiv.org/abs/2508.09665v1
• Date: Wed, 13 Aug 2025 09:53:23 GMT
• Title: Social-Sensor Identity Cloning Detection Using Weakly Supervised Deep Forest and Cryptographic Authentication
• Authors: Ahmed Alharbi, Hai Dong, Xun Yi,
• Abstract summary: We introduce a novel method for detecting identity cloning in social-sensor cloud service providers.<n>Our proposed technique consists of two primary components: 1) a similar identity detection method and 2) a cryptography-based authentication protocol.<n>Our experiments on a large real-world dataset demonstrate the feasibility and superior performance of our technique.
• Score: 5.911768189309895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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