  The data revolution holds significant promise for the health sector. Vast
amounts of data collected from individuals will be transformed into knowledge,
AI models, predictive systems, and best practices. One area of health that
stands to benefit greatly is the genomic domain. Progress in AI, machine
learning, and data science has opened new opportunities for genomic research,
promising breakthroughs in personalized medicine. However, increasing awareness
of privacy and cybersecurity necessitates robust solutions to protect sensitive
data in collaborative research. This paper presents a practical deployment of a
privacy-preserving framework for genomic research, developed in collaboration
with Lynx$.$MD, a platform for secure health data collaboration. The framework
addresses critical cybersecurity and privacy challenges, enabling the
privacy-preserving sharing and analysis of genomic data while mitigating risks
associated with data breaches. By integrating advanced privacy-preserving
algorithms, the solution ensures the protection of individual privacy without
compromising data utility. A unique feature of the system is its ability to
balance trade-offs between data sharing and privacy, providing stakeholders
tools to quantify privacy risks and make informed decisions. Implementing the
framework within Lynx$.$MD involves encoding genomic data into binary formats
and applying noise through controlled perturbation techniques. This approach
preserves essential statistical properties of the data, facilitating effective
research and analysis. Moreover, the system incorporates real-time data
monitoring and advanced visualization tools, enhancing user experience and
decision-making. The paper highlights the need for tailored privacy attacks and
defenses specific to genomic data. Addressing these challenges fosters
collaboration in genomic research, advancing personalized medicine and public
health.
