Hybrid Cloud Data Segmentation for Confidentiality-Aware Placement
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Solution Overview
Problem
Current solutions fail to effectively characterize unknown data and identify the optimal cloud type for storing or analyzing it in a hybrid cloud environment, lacking the ability to dynamically segment data based on confidentiality requirements and resource availability.
Innovation Solution
A method involving random sampling, pre-processing with machine learning, and confidentiality modeling to segment data between private and public clouds, considering hidden correlations and resource availability, with dynamic re-segmentation based on data changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is stored in public cloud to leverage robust computation resources, then productivity and resource utilization are improved, but data confidentiality and security deteriorate
Solution Approach 1:
The patent segments data into different categories (sensitive data, non-sensitive data, intermediate data) and assigns them to different cloud environments (private cloud, public cloud) based on their confidentiality requirements. This allows productive use of public cloud resources for non-sensitive data while maintaining security for sensitive data.
Solution Approach 2:
The patent applies different quality attributes to different parts of the data system by implementing location-aware data placement policies. Sensitive data receives high-security treatment in private cloud, while non-sensitive data leverages public cloud resources, optimizing both security and productivity for each data type.
2Reliability
If data segmentation is implemented based on confidentiality, then data security is improved, but system complexity increases
Solution Approach 1:
The patent implements automated data characterization and segmentation systems that self-manage the classification and placement of data based on predefined policies. This reduces manual intervention and simplifies operation despite the underlying complexity of multi-cloud data management.
Solution Approach 2:
The patent uses configurable parameters and policies to control data segmentation behavior, allowing organizations to adjust security levels and placement rules without redesigning the entire system. This makes the complex segmentation process adaptable and easier to manage.
3Measurement precision
If unknown data is characterized and segmented, then data management precision is improved, but processing time increases
Solution Approach 1:
The patent performs data characterization and segmentation in advance before data processing operations. By pre-classifying data and determining optimal placement locations beforehand, the system avoids time-consuming decisions during actual data processing, thus improving both accuracy and efficiency.
Solution Approach 2:
The patent employs machine learning models and automated algorithms to perform data characterization, replacing manual or rule-based approaches. This substitution of mechanical/classification systems with intelligent algorithms improves both the precision of data classification and the speed of processing.
Data Source
AI summary
A computer hardware system associated with a hybrid cloud including a private cloud and a public cloud has a hardware processor configured to perform the following executable operations. A plurality of data sets of unprocessed data is randomly sampled to generate a plurality of random samples respectively associated with each of the plurality of data sets. The plurality of random samples is pre-processed, using a machine learning engine, to identify hidden correlations contained therein. The hidden correlations are evaluated against a confidentiality model to characterize each of the plurality of segments as to confidentiality. The data sets are segmented by assigning the data sets to one of the private cloud or the public cloud based upon the evaluating. Data sets assigned to the private cloud based upon the segmenting are processed using the private cloud.