DATA PRIVACY IN CLOUD ENVIRONMENTS: BEST PRACTICES FOR SAFEGUARDING SENSITIVE INFORMATION IN DISTRIBUTED DATA SCIENCE WORKFLOWS

The exponential growth of cloud-based data science platforms has created unprecedented challenges in safeguarding sensitive information while maintaining analytical capabilities in distributed environments.This article presents a comprehensive framework for protecting data privacy in cloud-based data science workflows, examining both technical solutions and organizational best practices.The article investigates advanced privacy-preserving techniques, including differential privacy, homomorphic encryption, and secure data anonymization methods, alongside robust security infrastructures encompassing access controls, real-time monitoring