Protect sensitive patient information with our advanced de-identification solution.
PrivaSure seamlessly combines the robust Safe Harbor method, the precision of Expert Determination, and reversible Tokenisation into a single automated platform — giving your organization comprehensive HIPAA and GDPR-compliant data privacy without sacrificing research utility.
Whether you are preparing datasets for secondary research, sharing data with external partners, or meeting regulatory audit requirements, PrivaSure provides the full de-identification traceability needed for institutional accountability.
Watch PrivaSure transform a raw patient record into a fully de-identified research-ready dataset — applying Safe Harbor removal, Expert Determination risk scoring, and tokenisation simultaneously.
Prepare de-identified datasets for academic research and AI model training while maintaining full regulatory compliance.
Share clinical data across systems, regions and partners without exposing Protected Health Information.
Meet regulatory requirements for data sharing, actuarial analysis and population health research.
Prepare patient-level datasets for drug development and pharmacovigilance while protecting privacy.
PrivaSure gave us the confidence to share our oncology cohort data with three international research partners — something we had been unable to do for two years due to PDPL uncertainty. The Expert Determination methodology and audit trail were exactly what our DPO needed.
We processes over 2 million patient records annually across six GCC markets. PrivaSure handles the entire de-identification pipeline without any manual review step — the lineage trail it generates has passed every regulatory audit we have faced.
Every implementation is different. These are the questions we hear most often — answered directly, without the sales wrapper.
Safe Harbor is the right default for most clinical datasets — it removes 18 defined PHI identifiers and requires no statistical justification. Expert Determination is appropriate when you need to demonstrate a statistically negligible re-identification risk, typically for complex datasets being used in academic research or shared across jurisdictions. PrivaSure runs both methods simultaneously and recommends the appropriate approach based on your dataset characteristics and destination.
PrivaSure uses a trained clinical NLP model to identify and redact PHI in free-text fields — including discharge summaries, clinical notes, radiology reports, and pathology narratives. The model is trained on GCC and international clinical corpora and achieves >99.2% recall on named entity recognition across Arabic and English clinical text.
When Tokenisation is applied, yes. PrivaSure generates a token map stored in your own secure vault — meaning only your organisation can reverse the tokenisation. The de-identified dataset leaving your environment contains no re-identification pathway. This is particularly useful for longitudinal studies where you need to enrich the de-identified dataset with future clinical events.
For cloud deployments with standard HL7 FHIR or structured data sources, PrivaSure is typically live within 3–4 weeks. On-premise deployments with custom data pipelines run 6–10 weeks. DICOM anonymisation workflows are configured separately and typically add 1–2 weeks. Full enterprise deployments with multiple source systems are scoped individually.
PrivaSure's Expert Determination methodology is aligned with the UAE Personal Data Protection Law (PDPL) anonymisation standard. The platform generates a documented risk assessment report for every de-identification run — the exact format required by the UAE data protection authority for demonstrating that data has been rendered anonymous under Article 2(c) of the PDPL.