The data revolution presents unprecedented potential for advancements in the healthcare sector, particularly within genomic research. On one hand, the continuous accumulation of large-scale individual genomic data serves as the foundation for the development of artificial intelligence-based innovations and digital health technologies. On the other hand, genomic data raises unique security and privacy risks requiring structured threat modeling and privacy-preserving measures. This work presents an implementation of a privacy-preserving framework specifically designed for genomic research, integrated into a real-world secure platform for medical data collaboration. The proposed framework addresses critical security and privacy vulnerabilities, enabling the controlled sharing and analysis of genomic datasets while mitigating risks associated with data breaches. By leveraging advanced privacy-preserving algorithms, the framework protects privacy while maintaining data utility. A key aspect of this approach is the strategic trade-offs between data sharing and privacy, providing stakeholders with quantifiable metrics to assess privacy risks and inform data-sharing decisions. The implementation within a real-world platform encompasses encoding genomic data into binary formats and introducing controlled noise to preserve key statistical attributes. This ensures the integrity of research outcomes while enabling privacy-aware computational analyses. Additionally, the envisioned framework integrates real-time data monitoring and advanced visualization tools, optimizing user experience and decision-making processes. Given the unique characteristics of genomic data, our work underscores the necessity for tailored privacy attacks and corresponding defenses to safeguard sensitive information effectively. By addressing these challenges, the proposed solution aspires to foster a global research ecosystem in genomics, ultimately accelerating breakthroughs in personalized medicine and public health.