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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2508.09665 |
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| _version_ | 1866908487769915392 |
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| author | Alharbi, Ahmed Dong, Hai Yi, Xun |
| author_facet | Alharbi, Ahmed Dong, Hai Yi, Xun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09665 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Social-Sensor Identity Cloning Detection Using Weakly Supervised Deep Forest and Cryptographic Authentication Alharbi, Ahmed Dong, Hai Yi, Xun Cryptography and Security Machine Learning Social and Information Networks H.3; E.3; I.2; I.7 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. |
| title | Social-Sensor Identity Cloning Detection Using Weakly Supervised Deep Forest and Cryptographic Authentication |
| topic | Cryptography and Security Machine Learning Social and Information Networks H.3; E.3; I.2; I.7 |
| url | https://arxiv.org/abs/2508.09665 |