Data De-identification
& PII/PHI Redaction for
Enterprise AI Datasets

Data de-identification is the process of removing or masking personally identifiable information (PII) and protected health information (PHI) from datasets so individuals cannot be identified. Entity types covered include: faces, license plates, names, dates of birth, addresses, phone numbers, email addresses, National ID numbers, account numbers, medical record numbers, biometric markers, vehicle identification, and any other client-defined identifiers. This applies across images, video frames, DICOM files, PDFs, and structured data formats.

Faces and license plates are detected using a combination of automated detection models and manual specialist review. Each identified region is masked using the client's preferred method — pixelation, solid fill, blur, or black-box redaction. Edge cases such as partially visible faces, occlusion, low-light frames, and non-standard plate formats are handled by trained specialists with project-specific guidelines. All applied redactions are logged per record for audit traceability.

Our workflows are HIPAA-Aligned and designed to support both HIPAA Safe Harbor (18 PHI identifiers removed) and Expert Determination methods depending on project requirements. We operate under ISO 27001-Aligned security controls, require NDAs from all personnel with project access, implement role-based access controls, and maintain full audit trails for every record processed. We also support GDPR-Aligned de-identification for European datasets, and region-specific compliance for Canadian (PIPEDA-conscious) and APAC regulations.

We support de-identification across a wide range of media types and formats: images (JPEG, PNG, TIFF, BMP), video (MP4, AVI, MOV, frame sequences), medical imaging (DICOM — both pixel and metadata tag scrubbing), documents (PDF, DOCX, scanned records), structured data (CSV, JSON, XML, EHR exports), and audio/transcripts. DICOM de-identification includes both pixel-layer anonymization and removal of embedded PHI tags from the metadata fields per DICOM PS3.15 standards.

Accuracy is measured through our 3-tier QA pipeline: Tier 1 is specialist self-check and peer review targeting 95%+ redaction completeness. Tier 2 runs automated entity re-detection on the redacted output to surface any residual PII/PHI — flagging missed instances for human correction. Tier 3 is a QA Lead expert audit with random sampling (10–20% per batch, 100% for healthcare-critical projects) and a client acceptance review of sample records before full batch delivery. Final delivery accuracy is contractually maintained at 99.8%.

Large or continuous de-identification projects are managed through structured task allocation, batch-based processing with defined QA checkpoints, and dedicated project teams for each client engagement. For ongoing pipelines — such as live EHR systems, recurring AV datasets, or active learning loops — monthly retainer engagements provide a fixed-capacity dedicated team operating within your SLAs. All batches include redaction logs, entity-type breakdowns, and delivery confirmations with full traceability.

All data is transferred over encrypted, client-approved channels (SFTP, secure cloud storage, VPN access). Specialists access datasets through permission-scoped, role-based systems — no local storage or downloading to personal devices is permitted. Automated security monitoring runs continuously across all project environments. Upon project completion, all source data is securely deleted per client instructions and confirmed with a destruction certificate. These controls are aligned with ISO 27001-Aligned, HIPAA-Aligned, and GDPR-Aligned security requirements. See full details in our guide to annotation governance & security controls.

De-identification pricing depends on media type, entity density, volume, and review depth. Common models include per-image or per-frame (AV and surveillance), per-record or per-page (documents and medical records), hourly (complex multi-modal or mixed-entity datasets), and monthly retainer (ongoing pipelines). Our India-based team typically offers 50–60% savings versus equivalent US, UK, or EU de-identification providers. All engagements include a free pilot batch before commitment. Request a tailored de-identification quote based on your dataset type and volume.

Yes — this is one of the most common configurations for healthcare AI and autonomous vehicle teams. De-identification runs as a preprocessing step before annotation (e.g., DICOM face blurring before radiology region-of-interest labeling, or dashcam plate removal before bounding box labeling). Both services are delivered under one NDA, one SLA, and one compliance framework. See how we combine these in our bounding-box annotation service and full data labeling services pages.

Automated de-identification tools typically achieve 80–88% accuracy — missing edge cases, unusual PII formats, and context-dependent identifiers. Our human-in-the-loop approach combines automated detection with trained specialist review and 3-tier QA, reaching 99.8% accuracy. We handle low-contrast images, unusual entity types, handwritten records, partially visible identifiers, and multi-language documents that pure automation misses. Every project begins with a free pilot so you can verify quality against your specific dataset before committing to full-scale processing.