OPTIMIZATION OF MACHINE LEARNING METHODS FOR DE-ANONYMIZATION IN SOCIAL NETWORKS

OPTYMALIZACJA METOD UCZENIA MASZYNOWEGO DO DEANONIMIZACJI W SIECIACH SPOŁECZNOŚCIOWYCH
Smailov N. Uralova F. Kadyrova R. Magazov R. Sabibolda A.
2025 Politechnika Lubelska

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Srodowiska
2025 #15 Issue 1 101 - 104 pp.

In recent years, social networks have struggled to meet user protection and fraud prevention requirements under unpredictable risks. Anonymity features are widely used to help individuals maintain their privacy, but they can also be exploited for malicious purposes. In this study, we develop a machine learning-driven de-anonymization system for social networks, with a focus on feature selection, hyperparameter tuning, and dimensionality reduction. Using supervised learning techniques, the system achieves high accuracy in identifying user identities from anonymized datasets. In experiments conducted on real and synthetic data, the optimized models consistently outperform baseline methods on average. Even in cases where they do not, significant improvements in precision are observed. Ethical considerations surrounding de-anonymization are thoroughly discussed, including the responsibility of implementation to maintain a balance between privacy and security. By proposing a scalable and effective framework for analyzing anonymized data in social networks, this research contributes to improved fraud detection and strengthened Internet security.

data analysis , de-anonymization , machine learning , social networks , user privacy

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Satbayev University, Department of Radio Engineering, Electronics and Space Technologies, Almaty, Kazakhstan
Al-Farabi Kazakh National University, Department of Cybersecurity and Cryptology, Almaty, Kazakhstan
Institute of Mechanics and Machine Science named by academician U.A. Dzholdasbekov, Almaty, Kazakhstan
Al-Farabi Kazakh National University, Department of Artificial Intelligence and Big Data, Almaty, Kazakhstan
Almaty Academy of Ministry of Internal Affairs, Department of Cyber Security and Information Technology, Almaty, Kazakhstan

Satbayev University
Al-Farabi Kazakh National University
Institute of Mechanics and Machine Science named by academician U.A. Dzholdasbekov
Al-Farabi Kazakh National University
Almaty Academy of Ministry of Internal Affairs

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