Lenticular View Mechanism for Context-Aware Data Security
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Solution Overview
Problem
Existing database security techniques lack flexibility and fine-grained access control for sensitive data, often relying on binary approaches that provide zero feedback or require user awareness of data modification techniques, failing to maximize utility and comply with regulatory requirements.
Innovation Solution
The implementation of lenticular views, where sensitive data is transformed based on query context, using transformation techniques registered by privacy administrators, allowing only authorized users to access modified data forms, ensuring compliance with privacy policies without modifying existing database applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensitive data is overwritten with random data to protect privacy, then data security is improved, but data utility is lost completely (zero feedback to end-user)
Solution Approach 1:
The patent applies dynamic transformation of sensitive data based on query context. Instead of static overwriting, the system transforms data dynamically according to the specific query being executed, the user's authorization level, and the sensitivity classification. This allows the same data to be presented in different forms (fully protected, partially protected, or unprotected) depending on the context, thereby maintaining both security and utility.
Solution Approach 2:
The system changes the transformation parameters based on query context and user authorization. Transformation techniques such as generalization, suppression, or distortion are applied with varying degrees of intensity depending on the sensitivity level and user credentials. This parameter-based control enables fine-grained balance between security and data utility.
2Loss of information
If sensitive data is partially modified to maintain some utility, then data utility is improved, but access control flexibility is reduced (user must know modification techniques)
Solution Approach 1:
The system automatically determines and applies the appropriate transformation technique based on the query context and user authorization without requiring user knowledge or configuration. The database server itself performs the classification and transformation, making the system self-service and eliminating the burden from users to understand complex modification techniques.
Solution Approach 2:
The patent introduces an intermediary transformation layer between the raw sensitive data and the user query results. This intermediary automatically applies the appropriate transformation based on predefined policies and query context, shielding users from the complexity of data modification techniques while maintaining controlled access to useful information.
3Ease of manufacture
If static transformation is applied to sensitive data, then implementation simplicity is improved, but adaptability to different query contexts is reduced
Solution Approach 1:
The system transitions from static to dynamic transformation by evaluating query context at runtime. The transformation applied to sensitive data varies based on the specific query, user authorization, and data sensitivity classification, enabling the system to adapt to different scenarios while maintaining a unified implementation framework.
Data Source
AI summary
A machine-implemented method and machine-readable media for transforming sensitive data in a database is provided. Sensitive data in the database are transformed based on a query context of a query. The query may also be transformed. The transformed query may be applied against the transformed sensitive data to construct a query result. The query result with the transformed sensitive data represents a lenticular view. The lenticular view represents a modified form of the sensitive data that an end-user is allowed access to.