Image Storage Segmentation for Distributed Systems
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Solution Overview
Problem
Organizations face high costs and compliance challenges when storing large amounts of image data, particularly due to the need to securely store sensitive information across distributed computing systems.
Innovation Solution
A method that analyzes images to identify sensitive and non-sensitive portions based on enterprise data and compliance policies, splits the images accordingly, and stores the sensitive portions in secure environments while storing the non-sensitive portions in less expensive cloud storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all image data is stored in secure on-premise servers, then data security and compliance are ensured, but storage costs and operational complexity increase significantly
Solution Approach 1:
The patent segments image data into sensitive and non-sensitive portions, storing sensitive data in secure on-premise environments and non-sensitive data in cloud storage. This segmentation allows the organization to maintain security for critical data while reducing overall storage complexity and costs by moving non-critical data to simpler cloud infrastructure.
Solution Approach 2:
The patent applies different storage qualities to different portions of data based on sensitivity. Sensitive data receives high-security local storage with strict access controls, while non-sensitive data is stored in cloud environments with standard security measures. This local quality approach optimizes both security and cost efficiency.
2Reliability
If sensitive and non-sensitive image portions are stored together in secure environments, then data security is maintained, but storage costs increase
Solution Approach 1:
The patent segments images into sensitive and non-sensitive portions using image analysis technology. By separating these portions, the system can apply different storage strategies: sensitive portions are stored in expensive secure environments, while non-sensitive portions are stored in cheaper cloud storage, thereby reducing overall storage costs while maintaining security for critical data.
Solution Approach 2:
The patent extracts non-sensitive data from images and separates it from sensitive data. This extraction allows the non-sensitive portions to be stored in cost-effective cloud environments, reducing the quantity of data that requires expensive secure storage while maintaining complete security for the sensitive portions.
3Quantity of substance
If image data is split and stored across different computing environments, then storage costs are reduced, but system complexity and data retrieval complexity increase
Solution Approach 1:
The patent introduces an intermediary system that manages the splitting, storage, and recombination of image portions across different computing environments. This intermediary handles the complexity of distributed storage by automatically analyzing images, separating sensitive and non-sensitive data, managing storage locations, and recombining portions upon retrieval, thereby reducing the perceived complexity for users.
Solution Approach 2:
The patent merges the functions of image analysis, data separation, storage management, and data recombination into a unified system. This merging approach simplifies the overall process by providing a single interface for image storage and retrieval, even though the underlying data is distributed across multiple environments.
4Quantity of substance
If only non-sensitive image portions are stored in cloud storage, then storage costs are reduced, but compliance with data privacy regulations may be compromised
Solution Approach 1:
The patent segments images to identify and separate sensitive data portions that require compliance with data privacy regulations from non-sensitive portions. By ensuring that sensitive data remains in secure on-premise storage while only non-sensitive data is moved to cloud storage, the system reduces costs while maintaining compliance assurance for regulated data.
Solution Approach 2:
The patent applies different storage qualities and security measures to different data portions based on their sensitivity and compliance requirements. Sensitive data receives high-security local storage with strict access controls to ensure compliance, while non-sensitive data can be stored in cloud environments with standard security measures, optimizing both compliance and cost efficiency.
Data Source
AI summary
Provided is a method, system, and computer program product for storing images across multiple distributed computing systems according to image sensitivity. The method comprises identifying an image and analyzing the image to identify sensitive information in the image. The method further comprises splitting the image into a sensitive portion and a non-sensitive portion. The method further comprises storing the sensitive portion of the image in a first distributed computing system and storing the non-sensitive portion of the image in a second distributed computing system.