Hierarchical Encryption for Hybrid-Cloud Data Storage
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Solution Overview
Problem
Existing encryption techniques for data storage often use single or static encryption methods, leading to excessive encryption wasting resources and causing poor system performance, or insufficient encryption resulting in security risks.
Innovation Solution
A method utilizing trained machine learning models to parse database storage requests, determine sensitivities of request elements, and identify appropriate encryption techniques for each element, optimizing encryption for performance, efficiency, and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single or static encryption technique is applied to each data set, then security coverage is ensured across all data, but computing resources are wasted and system performance deteriorates due to excessive encryption
Solution Approach 1:
The patent applies different encryption techniques to different data elements based on their sensitivity levels. Machine learning models analyze each data element and assign appropriate encryption methods, so that highly sensitive data receives stronger encryption while less sensitive data uses lighter encryption, optimizing the balance between security and performance.
Solution Approach 2:
The patent transitions from static encryption to dynamic encryption by using machine learning models that can adaptively determine encryption techniques based on data characteristics. The encryption approach changes dynamically according to the sensitivity assessment of each data element, allowing the system to optimize security measures in real-time.
2Device complexity
If a single or static encryption technique is applied to each data set, then implementation simplicity is maintained, but security risks arise due to insufficient encryption for sensitive data
Solution Approach 1:
The patent enables the system to automatically assess data sensitivity and select appropriate encryption techniques without manual intervention. Machine learning models autonomously analyze data elements and determine the most suitable encryption methods, making the complex security management self-service and reducing the burden on users while maintaining high security standards.
Solution Approach 2:
The patent performs sensitivity assessment and encryption technique selection before actual data storage. Machine learning models pre-analyze data elements to determine their sensitivity levels and assign appropriate encryption methods in advance, so that when data is stored, the optimal encryption is already in place without requiring complex real-time decisions.
3Reliability
If separate encryption techniques are enforced for different data types (text and image), then data-specific security requirements are met, but system complexity increases and performance decreases
Solution Approach 1:
The patent uses a universal machine learning-based framework that can handle multiple data types (text, images, and other formats) through a single system. The ML models are trained to recognize sensitivity patterns across different data types and apply appropriate encryption techniques uniformly, eliminating the need for separate encryption systems for each data type while still meeting data-specific security requirements.
Data Source
AI summary
Techniques for hierarchical encryption for data storage are disclosed, in one or more embodiments. These techniques include parsing an electronic database storage request, based on the syntax of the request, to identify a plurality of request elements and determining, using one or more trained machine learning (ML) models, one or more sensitivities associated with the plurality of request elements. The techniques further include identifying one or more encryption techniques for the plurality of request elements based on the one or more sensitivities, encrypting data associated with the database storage request using the identified one or more encryption techniques, and storing the encrypted data and one or more associated encryption keys in an electronic database, using the electronic database storage request.