Data Representation Generation Without Content Access
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Solution Overview
Problem
In large-scale data centers, managing and troubleshooting computing resources in protected areas is challenging due to restricted access, making it difficult to share useful data for maintenance and troubleshooting without exposing sensitive information.
Innovation Solution
Implementing a data clustering service that generates a data representation using a K-means cluster model, allowing analysis and regeneration of data in the protected area without exposing actual content, using cluster identifiers and confidence scores to ensure secure data handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data is made accessible for troubleshooting and management in protected areas, then ease of operation and productivity improve, but data security and confidentiality deteriorate
Solution Approach 1:
The patent introduces an intermediary processing system that receives data from protected areas, applies automated desensitization techniques (masking, aggregation, generalization), and delivers processed data to users. This mediator enables troubleshooting and management operations while preventing direct exposure of sensitive data, thus resolving the contradiction between accessibility and security.
Solution Approach 2:
The system creates processed copies of sensitive data through automated desensitization rather than allowing direct access to original data. These copies retain necessary operational value for troubleshooting and management while having sensitive information removed or masked, enabling ease of operation without compromising data security.
2Object-affected harmful factors
If access restrictions are enforced in protected areas, then data security improves, but ease of operation and troubleshooting capability worsen
Solution Approach 1:
The automated desensitization system acts as an intermediary that maintains security restrictions while enabling operational access. It processes data to remove sensitive information before delivery, allowing troubleshooting activities to proceed without requiring relaxed security protocols, thus maintaining security protection while improving ease of operation.
Solution Approach 2:
The system implements self-service automated desensitization that operates without manual intervention, automatically identifying and masking sensitive data elements. This enables security protection to be maintained while troubleshooting personnel can independently access necessary non-sensitive data, reducing the operational burden imposed by strict security restrictions.
3Productivity
If sensitive data is exposed for analysis and management, then productivity and decision-making improve, but data confidentiality and security deteriorate
Solution Approach 1:
The system creates desensitized copies of sensitive data that can be freely analyzed and used for management decisions without risking the confidentiality of the original data. These processed copies enable full productivity benefits of data analysis while the original sensitive data remains protected, eliminating the trade-off between productivity and confidentiality.
Solution Approach 2:
An automated desensitization intermediary processes data before it reaches management personnel, enabling productive analysis and decision-making with non-sensitive information while preventing direct exposure of confidential data. This mediator maintains data confidentiality while delivering the productivity benefits of data-driven management.
Data Source
AI summary
Techniques for generating a data representation without access to content are described. A method for generating a data representation without access to content comprises receiving a request to analyze one or more data items in a protected area of the provider network, sending the request to the protected area of the provider network, wherein the cluster model is used to identify a cluster identifier associated with each of the one or more data items, receiving the cluster identifier associated with each of the one or more data items, and regenerating each of the one or more data items based on the cluster identifier.