Decentralized Token Map for Secure Credit Card Data
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Solution Overview
Problem
Current centralized tokenization systems face security risks and inefficiencies due to the storage of millions of complete credit card numbers, which attracts hackers and poses challenges in data availability and scalability.
Innovation Solution
Implementing a decentralized tokenization architecture that uses a pre-generated token map to associate tokens with portions of credit card numbers, allowing for secure tokenization and detokenization without the need for centralized databases, thereby reducing the risk of data breaches and improving system scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a centralized database stores millions of complete credit card numbers for tokenization, then tokenization functionality is achieved, but security risks increase and the system becomes vulnerable to cyber attacks
Solution Approach 1:
The patent segments the credit card number into multiple parts (e.g., first six digits, middle digits, last four digits) and stores only portions in the centralized database rather than complete numbers. This reduces the security risk while maintaining tokenization functionality, as the database contains fragmented information that is less valuable to attackers.
Solution Approach 2:
The patent extracts and removes the most sensitive portions of credit card numbers from centralized storage. By storing only non-sensitive segments in the centralized database and keeping sensitive segments distributed or encrypted, the system maintains tokenization capabilities while minimizing security vulnerabilities.
2Reliability
If a centralized database stores complete credit card numbers, then tokenization can be performed, but the system complexity and data exposure increase
Solution Approach 1:
The patent divides credit card data into segments, storing only necessary portions centrally and distributing other portions. This segmentation reduces the complexity of securing and managing large volumes of complete card data while preserving tokenization functionality through coordinated access to segmented data.
3Reliability
If millions of complete credit card numbers are stored centrally, then comprehensive tokenization is achieved, but the system becomes a prime target for hackers
Solution Approach 1:
By segmenting credit card numbers and storing only portions centrally, the patent maintains tokenization coverage while significantly reducing the value of the centralized database to potential attackers. The segmented data stored centrally lacks the completeness needed for fraudulent use.
Solution Approach 2:
The patent extracts and removes sensitive segments from centralized storage, keeping only non-sensitive or less-sensitive portions in the central database. This extraction approach maintains tokenization functionality while eliminating the primary target that would attract cyber attackers.
4Reliability
If a centralized database is used to store tokenization data, then data availability is maintained, but scalability and performance suffer
Solution Approach 1:
The patent segments data storage between centralized and distributed systems. The centralized database stores segmented portions for availability, while distributed systems store other portions for scalability. This segmentation allows the system to maintain data availability while improving scalability and performance through distributed architecture.
Data Source
AI summary
Various devices, methods, systems, and computer readable storage are provided for tokenizing data. In some examples, credit card numbers are tokenized using a pre-generated token map and absent the use of a networked database that stores a relatively large quantity of credit card numbers in a central location. The token map may be generated by a token map generator such that the token map can be used by a tokenizer to replace a portion of an account number with a token, and by a detokenizer to replace the token with the original portion of the account number. A pre-parser and parser may also be used to locate an account number and/or token in a message received over a network.