The propagation of community created information will demand efficient systems for encrypted data sharing and priority guidance. An inclusive model will be introduced to confront these challenges by ensuring both confidentiality and authenticity of shared information while offering customized ranking suggestions. The recommended system will integrate cryptographic methods for securing and verifying shared information, securing it against unauthorized entry and alteration. Additionally, user-based recommendation approach will be implemented to generate customized ranking suggestions taking into consideration user choices and interactions. By embedding privacy protection methods leveraging individualized suggestion models, system will deliver a resilient solution for protected exchange and evaluation of Social Media data, promoting improved user interactions and confidence in social media platforms. Trials will illustrate the success rate and operational capability of the proposed structure concerning data protection, recommendation preciseness, and system functionality emphasizing its applicability for real-life integration into social media settings.