Hybrid Data Storage with Per-Element Privacy Tagging
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Solution Overview
Problem
Existing data storage and processing systems fail to implement custom policies for individual data elements, such as privacy policies required by end users or governmental regulations, leading to inadequate data security and inefficient use of storage and processing resources.
Innovation Solution
The implementation of hybrid data storage and normalization methods that allow data elements to be stored and processed based on custom policies at a per-data-element level, using policy constraint meta-data to determine whether data should be stored on a client or a server, thereby maximizing server infrastructure utilization and ensuring compliance with privacy regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored uniformly at the per-data-set level (either fully on-premise or fully in the cloud), then the system is simple to manage, but it cannot implement custom privacy policies at the per-data-element level and cannot optimize storage based on sensitivity
Solution Approach 1:
The patent segments data into individual data elements with unique identifiers, allowing each element to be tagged with specific privacy policy metadata. This enables granular control over storage location and access permissions for each data element rather than treating entire data sets uniformly, resolving the contradiction between adaptability and complexity by organizing data in a structured, manageable way
Solution Approach 2:
The patent applies different storage policies and privacy constraints to different data elements based on their specific sensitivity and requirements. Each data element can have its own privacy policy tags that determine whether it should be stored on-premise or in the cloud, allowing local optimization of each data element's storage location based on its specific needs rather than applying a uniform policy to all data
2Reliability
If sensitive data elements are stored on the client (on-premise), then data security and privacy compliance are improved, but storage and processing resources on the client are consumed
Solution Approach 1:
The patent implements partial action by storing only the necessary portion of data (sensitive data elements requiring privacy protection) on the client, while storing less sensitive data elements on the cloud server. This avoids the excessive action of storing all data on-premise, optimizing the balance between security and resource consumption by applying privacy-preserving storage only where necessary
Solution Approach 2:
The patent introduces a hybrid storage architecture that acts as an intermediary between fully on-premise and fully cloud storage. This intermediary system uses privacy policy tags and metadata to automatically determine optimal storage locations, reducing the burden on client resources while maintaining security requirements through automated policy enforcement
3Productivity
If all data is stored on the server (cloud), then client processing resources are minimized, but the system cannot ensure compliance with privacy regulations like GDPR for sensitive data
Solution Approach 1:
The patent applies preliminary action by tagging data elements with privacy policy metadata and sensitivity classifications during data ingestion, before storage decisions are made. This preliminary categorization enables the system to automatically route sensitive data to on-premise storage and non-sensitive data to cloud storage, ensuring GDPR compliance is built into the storage architecture from the outset rather than requiring later interventions
Solution Approach 2:
The patent changes the storage location parameter dynamically based on data sensitivity and privacy policy requirements. By using metadata tags to classify data elements, the system can adjust where each data element is stored (client or server) based on its specific parameters, maximizing server utilization for non-sensitive data while maintaining compliance for sensitive data
Data Source
AI summary
A method and system for cloud distributed hybrid data storage and normalization are disclosed. The method may include obtaining a data set comprising data entities. A data entity may comprise data fields each containing a data element. The method may further include determining policy constraint meta-data for each of the data elements based on the storage policy constraint. The policy constraint meta-data may include a first meta-tag indicating the storage policy constraint for the data element. The method may further include determining whether a server satisfies the storage policy constraint based on the first meta-tag for the data element. When the server satisfies the storage policy constraint, the method may further include transmitting the data element to the server to store the data element on the server. When the server fails to satisfy the storage policy constraint, the method may further include, storing the data element on the client.