🛡️ PII Field Classification System in Java

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🛡️ PII Field Classification System in Java — 5 Clean Services

Protecting Personally Identifiable Information (PII) is a critical requirement in modern systems. Below is a clean microservice-driven approach to build a scalable PII detection pipeline using Java 💻, Kafka 🔄, and ML integration 🧠.




🔹 1. Field Ingestion Service

  • 🎯 Goal: Collect structured/unstructured data fields from multiple sources (DBs, APIs, files)
  • ⚙️ Tech Stack: Java + Spring Boot 🧑‍💻, Apache Kafka 🔄, REST APIs 🌐
  • 🧠 Real-Time Use Case: Continuously streams form fields from CRM, user registration portals, and logs to downstream classifier services
🛠 Implementation Steps:
  1. Develop Spring Boot REST clients to ingest data from REST APIs, JDBC, and file systems.
  2. Transform incoming field data into Kafka messages.
  3. Publish messages to a common topic like fields.raw.ingest.



🔹 2. Preprocessing & Normalization Service

  • 🎯 Goal: Standardize field names, strip noise, and handle null/empty values before classification
  • ⚙️ Tech Stack: Java + Apache Commons 💫, OpenNLP for basic tokenization ✂️
  • 🧠 Real-Time Use Case: Converts user_email, emailID, eMail → email to improve classification accuracy
🛠 Implementation Steps:
  1. Consume messages from Kafka topic fields.raw.ingest.
  2. Apply normalization functions: lowercasing, trimming, token merging, alias mapping (emailID → email).
  3. Handle null/default values and emit clean data to fields.normalized.



🔹 3. PII Classification Engine

  • 🎯 Goal: Detect and label fields as PII (e.g., email, SSN, phone) or non-PII using rule-based + ML hybrid
  • ⚙️ Tech Stack: Java + Weka/Smile 🧠, Regex Engine 🔍, Optional Python microservice via gRPC for ML model calls 🤝
  • 🧠 Real-Time Use Case: Flags fields like phoneNumber, ssn, dob as PII during form submission or schema scan
🛠 Implementation Steps:
  1. Consume normalized fields from Kafka topic fields.normalized.
  2. Apply regex and rule-based classification: detect fields like "email", "ssn", etc.
  3. Invoke ML model (Weka, Smile, or via gRPC to Python) for probabilistic classification.
  4. Publish results to fields.classified with label and confidence score.



🔹 4. Metadata & Audit Logger

  • 🎯 Goal: Log field names, classification results, confidence score, and timestamp for auditing 🕵️
  • ⚙️ Tech Stack: Java + Logback 📒, Elasticsearch 📊, MongoDB for audit trail 📂
  • 🧠 Real-Time Use Case: Tracks false positives and reclassifies if admin corrects the classification
🛠 Implementation Steps:
  1. Consume data from fields.classified topic.
  2. Log structured audit entries with field name, original value, label, confidence, timestamp.
  3. Persist data in Elasticsearch for quick search, and MongoDB for long-term storage.



🔹 5. Dashboard & Feedback Loop

  • 🎯 Goal: Provide UI for viewing PII detection, confidence levels, and allow manual override + feedback learning 🔁
  • ⚙️ Tech Stack: Java + Spring Boot + Thymeleaf 🌼, React/Angular for frontend 💻, Kafka for feedback stream 🔁
  • 🧠 Real-Time Use Case: Security analysts review flagged fields and correct misclassifications to train the model over time 🔄
🛠 Implementation Steps:
  1. Create frontend to show field → predicted label → confidence → allow override.
  2. Send feedback to Kafka topic fields.feedback with correction data.
  3. Feed corrections back into model training pipeline for continuous learning.



📊 Summary

  • 🚀 Ingestion → Normalization → Classification → Logging → Feedback — all built in modular, observable services.
  • 🧠 ML models and regex coexist to maximize precision and recall in PII detection.
  • 💡 Feedback loops improve model accuracy over time with analyst correction.



📋 Want Full Source Code + Architecture Diagram?

👉 Download the complete implementation + PDF (Google Drive)

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