Neural Network Tokenization for Data Privacy
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Solution Overview
Problem
Existing tokenization systems are not flexible enough to accommodate varied user requirements and data types, limiting their effectiveness in protecting sensitive data from leakage.
Innovation Solution
A system and method utilizing an intelligent trained artificial neural network to generate tokens based on the data type of the input data, supporting a wide range of tokenization techniques such as encryption, hashing, and randomization, and allowing for customizable configurations to meet diverse user needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing tokenization systems use fixed mechanisms, then implementation is simple, but flexibility and adaptability to varied user requirements deteriorates
Solution Approach 1:
The tokenization system dynamically adapts its behavior based on the input data type. The parser identifies the data type (credit card, social security number, medical record, etc.) and the system automatically selects the appropriate tokenization technique, making the system flexible without requiring complex manual configuration for each data type
Solution Approach 2:
The system changes its processing parameters based on the input data characteristics. Different data types trigger different tokenization algorithms with appropriate parameters, allowing the system to be versatile across multiple data types while maintaining manageable complexity through automated parameter selection
2Adaptability or versatility
If tokenization system supports extensive data types, then versatility improves, but system complexity and difficulty of implementation increases
Solution Approach 1:
The system segments the tokenization process into distinct modules: a parser that identifies data types, a router that selects appropriate tokenization techniques, and execution modules for each technique. This segmentation allows support for extensive data types while keeping implementation complexity manageable through modular design
Solution Approach 2:
The system implements a universal tokenization framework that handles multiple data types through a common architecture. The parser and technique selection mechanism serve all data types, while specific tokenization techniques are applied only when needed, achieving versatility without proportionally increasing overall system complexity
3Manufacturing precision
If manual configuration is required for each tokenization case, then precision and control improve, but ease of operation and time consumption deteriorates
Solution Approach 1:
The tokenization system performs self-configuration by automatically identifying the input data type through the parser and selecting the appropriate tokenization technique without requiring manual user input. This maintains tokenization precision through automated type-based selection while significantly improving ease of operation
Solution Approach 2:
The system uses feedback from the parser about the detected data type to automatically adjust the tokenization approach. This closed-loop mechanism ensures precise tokenization by adapting to the specific data type while eliminating manual configuration steps, improving both precision and ease of use
Data Source
AI summary
The present invention describes a system and method for tokenization of data. The system includes a receiver configured to receive a request for tokenization. The request for tokenization comprises an input data to be tokenized. The system also includes a parser configured to determine one or more datatype from the input data. The system further includes a trained artificial neural network to generate a token for the input data based on a tokenization technique corresponding to the datatype of the input data.