Autocomplete Term Classification for Sensitive Data Filtering
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Solution Overview
Problem
Existing autocomplete technologies lack fine control over filtering sensitive information, leading to potential leaks of confidential data, and manual blacklisting or whitelisting is costly and impractical for effective management.
Innovation Solution
Implementing a device with a classification unit that determines the class of a term from a database and a filter unit to block or allow term presentation based on user permissions, using a combination of rules and machine learning to categorize terms as sensitive or non-sensitive, thereby providing finer control over data filtration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual blacklisting or whitelisting is used to filter sensitive information, then control over data filtration is achieved, but deployment and maintenance costs are high and scalability is poor
Solution Approach 1:
The system automatically classifies terms using machine learning algorithms and rules engines, enabling self-service classification without manual intervention. The classification unit autonomously determines sensitivity levels and applies appropriate filtering actions, eliminating the need for manual blacklist/whitelist maintenance while maintaining reliable control over data filtration.
Solution Approach 2:
The patent replaces the mechanical manual process of creating and maintaining blacklists/whitelists with an automated electronic classification system. The machine learning model and rules engine substitute human operators, automatically analyzing terms, determining their classification, and applying filtering rules, thereby dramatically improving deployment and maintenance efficiency.
2Device complexity
If simple weighting or threshold parameters are used for filtering, then implementation is simple, but fine control over sensitive information filtering is insufficient
Solution Approach 1:
The patent segments the filtering mechanism into multiple hierarchical levels: classification units that categorize terms into different sensitivity levels, rule engines that apply specific filtering actions to each level, and machine learning models that enhance classification accuracy. This segmentation enables fine-grained control over filtering precision while keeping each individual component relatively simple and manageable.
3Measurement precision
If classification technology is used to filter auto-complete suggestions, then fine control over filtered elements is achieved, but system complexity increases
Solution Approach 1:
The classification unit serves multiple functions: it classifies terms by sensitivity, determines appropriate filtering actions, and integrates with the auto-complete suggestion system. This multi-functionality reduces the need for separate dedicated components for each function, thereby achieving fine control precision without proportionally increasing overall system complexity.
Data Source
AI summary
A class may be determined of a term from a database. The term may be blocked from being presented to a user, if the determined class does not include a permission for the user to view the term. The term may suggest a remainder of an incomplete query input by the user.