Cloud Data Classification for Secure Private-Public Migration
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Solution Overview
Problem
Cloud-computing environments face threats from cyberattacks, data breaches, and redundant data storage during cloud migrations, necessitating improved security and classification of private and public cloud data.
Innovation Solution
A system utilizing generative machine-learning models, such as GANs, to identify and classify cloud data as real, valid, private, or public, ensuring secure migration by transmitting data to appropriate cloud systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If cloud data is migrated without classification, then migration speed is improved, but security is worsened due to potential cyber threats and data breaches
Solution Approach 1:
The system performs preliminary classification of cloud data into private and public categories before migration occurs. Machine learning models analyze data characteristics, sensitivity, and security requirements in advance, enabling the migration process to route different data types through appropriate security channels, thus maintaining both speed and security
2Quantity of substance
If all cloud data is stored in the same cloud system, then storage efficiency is improved, but security is worsened due to inability to differentiate private and public data requirements
Solution Approach 1:
The system segments cloud data into distinct categories (private and public) based on classification results. Private data is routed to secure, isolated storage systems with restricted access, while public data can be stored in more accessible cloud environments. This segmentation maintains storage efficiency while addressing different security requirements of different data types
3Reliability
If cloud data is classified and routed to different systems, then security is improved, but system complexity is worsened
Solution Approach 1:
The system introduces an intermediary classification layer that automatically analyzes and categorizes cloud data using machine learning models. This intermediary component handles the complexity of differentiation, while the actual migration and storage processes remain relatively simple, as they only need to follow the classification decisions made by the intermediary system
4Measurement precision
If machine learning models are used for data classification, then classification accuracy is improved, but processing time is worsened
Solution Approach 1:
The system applies partial classification action by focusing machine learning analysis on critical security attributes and key data characteristics rather than performing exhaustive analysis on all data aspects. This selective approach achieves sufficient classification accuracy for security purposes while reducing overall processing time
Data Source
AI summary
A system includes a memory configured to store a set of source data. The system further includes processors operably coupled to the memory and configured to access the set of source data, execute a first machine-learning model of one or more generative machine-learning models trained to generate a first set of data based on the set of source data, execute a second machine-learning model trained to identify the first set of data and a second set of data as each corresponding to one of a set of valid data or a set of invalid data, and execute a third machine-learning trained to identify the set of valid data as corresponding to one of a set of private valid data or a set of public valid data. The processors transmit the set of valid data to one of a first or second cloud computing and storage system based on the identification.