Parallel Date-Time Tokenization Pipelines for Distributed Data Protection
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Solution Overview
Problem
Existing data protection methods fail to adequately account for the structural and characteristic differences in date, decimal, and floating point information formats, necessitating improved tokenization techniques that ensure security and efficiency in distributed network environments.
Innovation Solution
A distributed tokenization environment utilizing parallel tokenization pipelines, where data is processed in parallel through multiple tokenization engines and processing engines, employing static lookup tables and initialization vectors to enhance security and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional tokenization methods are used, then data protection is provided, but the structural and characteristic differences in date, decimal, and floating point formats are not adequately accounted for
Solution Approach 1:
The tokenization system is segmented into multiple specialized tokenization engines, each dedicated to specific data formats (date, time, decimal, floating point). Each engine handles its specific format using format-appropriate token tables and processing logic, enabling format-specific tokenization while maintaining overall system reliability
Solution Approach 2:
The system implements a universal tokenization framework that can handle multiple data formats through a common architecture. The framework includes a unified interface for receiving different data types and a centralized token table management system that serves all format-specific engines, achieving both versatility and reliability
2Productivity
If parallel tokenization pipelines are implemented, then processing time is reduced, but system complexity increases
Solution Approach 1:
The tokenization system is divided into multiple parallel pipelines, with each pipeline handling specific data formats independently. This segmentation allows simultaneous processing of different formats without interference, reducing total processing time while maintaining manageable complexity through modular design
Solution Approach 2:
The system transitions from sequential processing to parallel processing by adding a temporal dimension. Multiple tokenization operations execute concurrently across different time steps and processing threads, transforming a one-dimensional sequential workflow into a multi-dimensional parallel architecture that improves productivity
Data Source
AI summary
Data in various formats can be protected in a distributed tokenization environment. Examples of such formats include date and time data, decimal data, and floating point data. Such data can tokenized by a security device that instantiates a number of tokenization pipelines for parallel tokenization of the data. Characteristics of such data can be used to tokenize the data. For instance, token tables specific to the data format can be used to tokenized the data. Likewise, a type, order, or configuration of the operations within each tokenization pipeline can be selected based on the data format or characteristics of the data format. Each tokenization pipeline performs a set of encoding or tokenization operations in parallel and based at least in part on a value received from another tokenization pipeline. The tokenization pipeline outputs are combined, producing tokenized data, which can be provided to a remote system for storage or processing.