Big Data Column Masking via Biometric Authentication
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Solution Overview
Problem
Existing methods for securing big data access are inadequate due to the high volume of data, providing only one layer of protection, and failing to secure data during transfer, making big data vulnerable to security threats.
Innovation Solution
A system and method using a data access device that receives biometric data to authenticate users and provides selective access to big data columns based on pre-defined user privileges, with masking of columns based on data parameters, offering multiple layers of security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional database security methods are implemented, then data access control is provided, but the methods cannot be implemented for big data due to huge volume
Solution Approach 1:
The patent segments the big data table into multiple columns and applies different masking levels to different columns based on data parameters. This allows traditional security methods to be adapted for big data by processing data in manageable segments (columns) rather than treating the entire large dataset as a single unit, thereby resolving the contradiction between maintaining security reliability and adapting to big data volume.
2Ease of operation
If one layer of data protection is provided, then data access is controlled, but if the first layer is breached, big data becomes automatically accessible
Solution Approach 1:
The patent implements nested security layers by applying multiple levels of masking to different columns within the same data table. Sensitive columns have higher masking levels while less sensitive columns have lower levels. This creates a nested structure where breaching one layer does not provide automatic access to all data, as each column maintains its own protection level, thus strengthening overall data protection while maintaining operational ease.
3Ease of operation
If data is made accessible without restrictions, then ease of access is improved, but security vulnerability increases
Solution Approach 1:
The patent applies local quality by assigning different masking levels to different columns based on their specific data parameters and sensitivity. Rather than uniformly restricting or allowing access to the entire dataset, each column receives customized protection appropriate to its local characteristics. This enables selective accessibility where users can access less sensitive data freely while sensitive columns maintain stronger protection, balancing ease of access with security against harmful factors.
4Reliability
If all columns are fully protected, then security is enhanced, but data utility and accessibility are reduced
Solution Approach 1:
The patent changes the security parameter (masking level) dynamically based on data parameters such as sensitivity, data type, and user role. Rather than applying a fixed high level of protection to all columns, the system adjusts protection parameters to match the actual needs of each column. This ensures that security is enhanced where necessary while maintaining data utility and accessibility where full protection is not required, thereby preserving productivity.
Data Source
AI summary
In one embodiment, a method for providing access to big data is disclosed. The method includes receiving biometric data of a user to provide access to columns of a table storing the big data, wherein one or more columns of the table are masked based on one or more data parameters and authenticating the user by comparing the biometric data of the user with pre-stored biometric data, wherein the pre-stored biometric data is retrieved from a biometric database. Further, the method includes providing upon the authentication, selective access to each column of the table based on a pre-defined user privilege for each column.