Record-Level Sensitivity Data Storage in Hybrid Multi-Cloud
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Solution Overview
Problem
Current data storage solutions in networked computing environments lack the ability to efficiently manage record-level sensitivity, leading to overprovisioning of resources and potential data security breaches, as they typically categorize entire datasets by the sensitivity of the most sensitive record, rather than individual records, and struggle with conflicting sensitivity requirements across different regions and applications.
Innovation Solution
Implement a method to identify the sensitivity of each data record within a dataset and store it in a data center that matches its sensitivity level, allowing different records with varying sensitivity levels to be stored in different types and locations of data centers, thereby optimizing resource usage and ensuring compliance with legal and ethical requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entire datasets are categorized by the sensitivity of the most sensitive record, then data security is improved, but resource efficiency deteriorates due to overprovisioning
Solution Approach 1:
The patent segments the dataset at the record level, allowing individual records to be categorized by their own sensitivity rather than forcing the entire dataset to adopt the highest sensitivity level. This segmentation enables mixed-sensitivity datasets to be stored efficiently across different data centers with appropriate security levels matched to each record's actual sensitivity requirements.
Solution Approach 2:
The patent applies local quality by assigning different storage locations (data centers) to different records based on their specific sensitivity requirements. Instead of uniformly storing all records in high-security data centers, each record is placed in a data center whose security level matches that particular record's sensitivity, optimizing resource utilization while maintaining security.
2Loss of energy
If record-level sensitivity identification is implemented, then resource efficiency is improved, but system complexity increases
Solution Approach 1:
The patent performs preliminary action by identifying and categorizing the sensitivity level of each record before storage. This advance classification enables the system to automatically route records to appropriate data centers without requiring complex real-time decision-making during storage operations, thereby managing system complexity while achieving resource efficiency.
Solution Approach 2:
The patent introduces an intermediary classification mechanism that bridges the gap between raw data and storage infrastructure. This intermediary layer categorizes records by sensitivity and translates these categories into appropriate data center selections, simplifying the overall system architecture by decoupling the sensitivity assessment logic from the storage placement logic.
3Reliability
If high-security data centers are used for all data, then data security is improved, but cost increases
Solution Approach 1:
The patent applies local quality by matching each record's security requirements with the appropriate data center security level. Instead of uniformly deploying all records in high-security data centers, records are placed in data centers whose security level corresponds to their actual sensitivity, thereby reducing costs while maintaining adequate security for each record.
Solution Approach 2:
The patent effectively uses lower-cost storage resources for less sensitive records, analogous to using cheaper substitutes where appropriate. By categorizing records by sensitivity, the system can store less sensitive records in more economical data centers while reserving high-security data centers only for records that truly require them, optimizing the cost-security tradeoff.
Data Source
AI summary
An approach is provided for providing record-level sensitivity-based data storage in a networked computing environment. For each data record of a plurality of data records (e.g., rows) in a dataset, the record sensitivity is identified based on the data included in the data record, allowing different sensitivities to be identified for different records in the same dataset. A data center that has a data center sensitivity level that matches the record sensitivity is selected for the identified record sensitivity. Each data record is stored to a selected data center, with data records having different record sensitivities being stored in different data centers of different types, locations, etc.