Chapter 26 — PIIGuard
Based on: SIH1668 — Government PII Protection System
Organization: Ministry of Electronics and Information Technology (MeitY)
Skills Required:
- Natural Language Processing (NLP)
- Data privacy and protection techniques
- Information extraction and entity recognition
- Python programming (spaCy, transformers)
- Document processing (PDF, Word)
- Database management (SQL/NoSQL)
- Secure software development fundamentals
Project Description:
PIIGuard is an intelligent data privacy protection system designed to automatically identify, classify, and redact Personally Identifiable Information (PII) in documents, communications, and datasets. Employing advanced NLP techniques, the system supports diverse data formats and languages, enabling organizations to comply with data protection regulations like GDPR and India’s Data Privacy laws. PIIGuard can be integrated into enterprise workflows to prevent unauthorized data exposure while enabling audit and incident response capabilities.
Tech Stack:
- Python NLP libraries (spaCy, transformers)
- Document processing frameworks (PDFMiner, Tika)
- Machine learning models for PII detection and classification
- Database for storing PII metadata and redaction status
- Flask/FastAPI for RESTful services
- React or Vue for user-facing dashboards
- Secure storage and encryption mechanisms
Week-wise Plan:
Week 1: Requirement Analysis & Data Collection
- Define PII categories and data sources.
- Collect diverse datasets for training and validation.
- Setup development environment and tools.
- Deliverable: Data catalog and initial project specs.
Week 2: Document Ingestion & Preprocessing
- Build modules to extract text from PDFs, Word docs, emails.
- Implement text normalization, language detection, tokenization.
- Deliverable: Document ingestion pipeline.
Week 3: PII Detection Models
- Train and fine-tune machine learning models for PII detection (NER/RE).
- Combine rule-based and ML-based detection for optimal accuracy.
- Deliverable: PII detection model with evaluation.
Week 4: Redaction Engine
- Design redaction algorithms to mask/redact identified PII.
- Support configurable redaction levels and custom exceptions.
- Deliverable: Redaction module with user controls.
Week 5: Workflow & API Development
- Develop RESTful API for document submission and retrieval of redacted output.
- Support batch processing and streaming data redaction.
- Deliverable: Backend API with security controls.
Week 6: User Interface & Dashboard
- Create dashboard for monitoring redaction jobs, review, and audit trails.
- Implement user management and role-based access control.
- Deliverable: User dashboard prototype.
Week 7: Testing & Evaluation
- Conduct comprehensive tests on accuracy, false positives/negatives.
- Optimize performance and storage efficiency.
- Deliverable: Test reports and performance enhancements.
Week 8: Documentation & Deployment
- Finalize system documentation, user guides, and developer manuals.
- Containerize the application for cloud or on-prem deployment.
- Deliverable: Release-ready PIIGuard platform with deployment scripts.
Testing and Deliverables:
- Validate detection and redaction accuracy on sample and real-world datasets.
- Measure throughput and latency for batch and streaming workflows.
- Provide source code, trained models, user manuals, and video demos.
PIIGuard safeguards organizational data by automating and enforcing privacy protections, enabling compliance and reducing data leakage risks.