The Privacy Analytics Platform, which includes the core Eclipse software, is an enterprise-grade solution for de-identification and data governance, primarily serving the biotech and healthcare sectors. It is designed to enable organizations to safely use and share sensitive data, such as patient health information, while maintaining compliance with global privacy regulations like HIPAA and GDPR. The platform is built on a trusted, peer-reviewed de-identification methodology and is capable of performing HIPAA Expert Determinations.
Key Features and Capabilities:
- Data De-Identification: Supports both structured and unstructured data, including DICOM imaging and dense clinical text.
- Risk Analysis: Provides identifiability and re-identification risk analysis/intelligence with risk visualization tools.
- Compliance: Offers auditable proof of compliance with regulations like HIPAA (Expert Determination) and GDPR.
- Privacy Enhancing Technologies (PETs): Includes a range of flexible data transformation options such as Data Masking, Pseudonymization, Generalization, Date-shifting, Outlier Suppression, and Differential Privacy/Synthetic Data Creation.
- Data Governance: Enables data cataloguing, data governance enablement, and automated workflows for information sharing.
- Scalability: Proven to work at scale, protecting trillions of sensitive data records and handling terabyte-sized datasets.
Target Users and Use Cases:
- Target users are typically data privacy officers, data scientists, IT security teams, and legal/compliance professionals within large organizations.
- Primary use cases include enabling the secondary use of health data for R&D, clinical trial transparency, data commercialization, and achieving regulatory compliance for data sharing initiatives.
Deployment and Integration:
- The software can be installed on-premises or in a secure private cloud (Azure, AWS, Google Cloud).
- It offers APIs for integration into existing data pipelines and supports cloud-native cluster computing services like Amazon EMR and Azure Databricks.