This article explains a step‑by‑step approach to mask sensitive fields in API responses by defining desensitization rules in YAML, loading them into Java Maps, and applying recursive logic to traverse nested structures and replace data using regular expressions.
This article demonstrates how to build a reusable data‑desensitization component in Java by defining custom annotations, extending Hutool's DesensitizedUtil, and creating a Spring AOP aspect that automatically masks sensitive fields in returned objects such as pages, lists, or single entities.
This article explains why data desensitization is essential for modern enterprises, compares masking, obfuscation and encryption techniques, and provides step‑by‑step implementations for database, log, and output layers using MyBatis interceptors, Fastjson filters, and Spring MVC configuration.
The article demonstrates how to mask sensitive Java backend data by using Hutool's DesensitizedUtil for common types and a custom @Desensitization annotation with a DesensitizationTypeEnum‑driven Jackson serializer (or Fastjson ValueFilter) to apply flexible masking rules during JSON serialization.
This article explains data desensitization concepts, common masking rules, and demonstrates how to use the Hutool library and Jackson annotations in Java to mask sensitive fields such as IDs, phone numbers, and passwords, while also comparing alternative tools.
This article explains how to mask sensitive fields such as ID numbers, phone numbers, and addresses in Java APIs by creating a custom @Sensitive annotation, defining masking strategies, implementing a Jackson serializer, and testing the solution with a Spring REST controller, providing complete code examples and sample output.
This article introduces d18n, a Go‑based, cross‑platform data‑desensitization tool that supports multiple databases and file formats, explains common desensitization scenarios, details its sensitive‑data identification techniques—including keyword, regex, and NLP‑based DFA—and outlines six practical masking algorithms with export and import workflows.
This tutorial explains how to move data desensitization to the JSON serialization stage using Jackson, defines a @Sensitive annotation, custom strategies, a contextual serializer, and demonstrates the approach with a User class example that masks personal fields during serialization.
