This focuses on the security and privacy preservation of medical big data using soft computing techniques. As the volume of medical data continues to grow, ensuring its security and protecting patient privacy become paramount. The research aims to employ soft computing techniques to address these challenges effectively.

Soft computing methods, such as fuzzy logic, neural networks, and genetic algorithms, will be explored for their potential in securing medical big data and preserving patient privacy. These techniques can aid in data encryption, anonymization, access control, and anomaly detection, safeguarding sensitive medical information from unauthorized access and potential cyber-attacks.

This will evaluate the performance of different soft computing approaches in terms of data protection, computation efficiency, and scalability. Special emphasis will be placed on adhering to privacy regulations and ethical considerations concerning medical data.

The outcomes of this research have the potential to significantly impact the healthcare industry. By effectively implementing soft computing techniques, the security and privacy of medical big data can be enhanced, instilling confidence in patients, healthcare providers, and stakeholders regarding data confidentiality. Furthermore, the findings may contribute to the development of robust and privacy-preserving systems that promote data sharing and collaboration in medical research while upholding the highest standards of data protection.

By employing these methods, the study seeks to address data protection challenges and pave the way for safer and more reliable data management practices within the healthcare domain.

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