International Journal of Engineering, Technology and Computer Science

The rapid growth of social media platforms has generated massive volumes of user generated data, offering unprecedented opportunities for analyzing behavioral patterns, temporal dynamics, and interaction structures. However, the extraction of meaningful insights from such data introduces significant privacy risks, including re-identification, inference attacks, and unauthorized profiling. This study surveys and integrates major privacy preserving data-mining techniques applicable to social media usage analysis, emphasizing the balance between analytical utility and user confidentiality. Key approaches examined include differential privacy, secure multi-party computation, federated learning, homomorphic encryption, and data perturbation or synthetic-data generation. The paper discusses their applicability to core analytical tasks such as user behavior modeling, community detection, temporal trend analysis, and anomaly detection, highlighting the inherent privacy utility trade-offs. Additionally, the study outlines threat models relevant to social platforms and examines anonymization strategies for safely collecting, representing, and processing user activity data. An experimental framework is proposed for evaluating privacy-preserving analytics using real and synthetic datasets under varied privacy scenarios. The findings underscore the necessity of integrating privacy by design principles into modern social media data-mining pipelines to ensure ethically sound, secure, and analytically robust usage-pattern discovery.