Privacy-Preserving Healthcare Data Analytics

Use Case: Hospitals and research institutions want to analyze sensitive patient data (electronic health records, imaging, genomics) for disease prediction, drug discovery, and population health monitoring while ensuring HIPAA compliance and patient privacy through encryption, anonymization, and secure ML techniques.

Enable secure, large-scale healthcare analytics without compromising patient privacy.

Support federated learning and differential privacy for data protection.

Provide actionable insights for clinicians, researchers, and policymakers while maintaining compliance with HIPAA/GDPR.

This project develops a cloud-based privacy-preserving healthcare analytics platform. Patient data from multiple hospitals and IoT health devices is securely ingested, encrypted, and anonymized before processing. Federated learning allows ML models to be trained across distributed hospital data without moving sensitive records to the cloud.

Differential privacy ensures aggregated insights cannot reveal individual patient information. Clinicians and researchers can use dashboards for predictive analytics, disease progression forecasting, and public health monitoring, ensuring both innovation and trust.

GCP Service	Purpose
Cloud Healthcare API	Secure ingestion, storage, and interoperability of health data (HL7, FHIR, DICOM formats).
Cloud IoT Core	Connects IoT medical devices (e.g., wearable heart monitors, glucose sensors) for real-time data ingestion.
Cloud Pub/Sub	Reliable, low-latency event streaming of medical data between hospitals, edge devices, and analytics services.
Cloud Data Loss Prevention (DLP) API	Identifies and anonymizes sensitive data (names, SSNs, addresses) to ensure patient privacy.
Confidential VMs + Confidential GKE	Runs analytics and ML workloads in secure enclaves with memory-level encryption.
BigQuery	Secure storage and querying of large-scale anonymized healthcare datasets for research and trend analysis.
Vertex AI (Federated Learning + DP)	Trains ML models using federated learning and differential privacy without centralizing sensitive data.
Cloud Functions	Event-driven triggers for alerts (e.g., abnormal vital signs, compliance policy violations).
Cloud Key Management Service (KMS)	Encryption and secure key management for healthcare datasets.
Cloud Storage	Stores encrypted raw patient data, medical images, and long-term research datasets.
Cloud Monitoring + Operations Suite	Tracks system performance, compliance, and detects security anomalies in healthcare workflows.

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