Privacy Preservation in Apache Spark Big Data using AI Assistants

The Healthcare sector uses the growth of big data analytics and AI assistants based on Apache Spark to assist in clinical decision-making, predicting diseases, and managing healthcare. Even though the technol-ogies make it possible to process large amounts of patient data in a scalable and efficient manner, it is also important to note that these technologies pose a significant risk to privacy and security because healthcare information is sensitive and the distributed computing environment of Spark. The risks include illegal access, information leakage, re-identification, and privacy inference using AI models, which are very dangerous to patient confidentiality and regulatory compliance. Current Apache Spark systems do not provide much privacy protection that is specifically tailored to AI-mediated healthcare analytics. This paper presents a privacy-sensitive system of Apache Spark, which combines AI assistants with state-of-the-art privacy controls like anonymization, encryption, differential privacy, and controlled data access. The given approach allows ensuring secure, scalable, and privacy-conscious healthcare analytics with an acceptable analytic performance. The framework will facilitate the safe implementation of big data analyt-ics based on AI in healthcare settings through its ability to maintain high levels of privacy and adherence to medical standards and regulations.