Selective Data Encryption for NAS-to-Cloud Migration
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Solution Overview
Problem
Existing data encryption methods are inefficient and costly due to the need to encrypt entire datasets, despite varying levels of confidentiality within the data, and are vulnerable to modern computing power and quantum computing threats.
Innovation Solution
A method to identify and group data items based on confidentiality levels, applying appropriate encryption methods to each subset, and decrypting upon arrival at the destination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entire datasets are encrypted using traditional methods, then data confidentiality is maintained, but encryption efficiency and cost are reduced
Solution Approach 1:
The patent segments datasets into individual data items or smaller groups based on confidentiality levels. Each segment is then encrypted independently using appropriate encryption methods, rather than encrypting the entire dataset as a single unit. This segmentation approach maintains data confidentiality for each item while significantly improving encryption efficiency and reducing computational costs.
Solution Approach 2:
The patent applies different encryption methods and key lengths to different data items based on their specific confidentiality requirements. High-sensitivity data receives stronger encryption (e.g., 256-bit keys), while less sensitive data uses lighter encryption methods. This local quality approach ensures adequate protection for each data item without uniformly over-encrypting all data, thereby improving overall encryption efficiency.
2Reliability
If strong encryption methods with larger key sizes are used, then data security is improved, but computational cost and processing time increase
Solution Approach 1:
The patent determines the appropriate encryption strength for each data item based on its confidentiality level. Critical data items receive strong encryption with larger key sizes (e.g., 256-bit), while less critical items use weaker encryption methods. This local quality approach ensures high security for sensitive data while reducing computational cost and energy consumption for less sensitive data.
Solution Approach 2:
The patent dynamically adjusts encryption parameters such as key length and algorithm selection based on the confidentiality classification of each data item. By changing these parameters according to data sensitivity rather than using fixed strong encryption for all data, the system achieves appropriate security levels while optimizing computational resource usage and reducing processing time.
3Productivity
If selective encryption is implemented based on data sensitivity, then encryption efficiency improves, but system complexity increases
Solution Approach 1:
The patent segments data into confidentiality-based groups and applies encryption selectively to each segment. This segmentation simplifies the overall encryption process by breaking it into manageable parts with different encryption requirements, making the system more efficient while keeping the complexity of each individual encryption operation relatively simple and standardized.
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for selective dataset encryption is provided. The embodiment may include identifying one or more data items within one or more datasets to be moved from network attached storage to cloud-based storage. The embodiment may also include determining an encryption method to be performed on each data item based on a level of confidentiality or sensitivity of each data item. The embodiment may further include sorting each data item into one or more groups based on the determined encryption method. The embodiment may also include performing each determined encryption method to the corresponding group.