Decoy Data Access with Multi-Trust Scoring for Secure Storage
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing digital resource storage systems lack robust mechanisms to prevent unauthorized access and ensure data integrity by effectively distinguishing between authentic and unauthorized entities attempting to access sensitive information.
Innovation Solution
A method involving triply encrypted digital resources and a decentralized validation system that uses multiple trust score calculations based on user and entity behaviors, combined with decoy resource generation to deter and identify potential hackers, utilizing AI-driven models and blockchain integration for enhanced security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional encryption and access control mechanisms are used, then data security is partially improved, but the system cannot effectively distinguish between authentic and unauthorized entities, allowing hackers to bypass security measures
Solution Approach 1:
The patent segments the authentication process into multiple independent trust score calculations performed by different system components (application, data protection tool, data store). Each component evaluates specific behavioral aspects and contributes to the overall authentication decision, improving both security reliability and authentication precision through distributed verification
Solution Approach 2:
The system implements continuous feedback loops where user behaviors are recorded, analyzed, and used to dynamically adjust trust scores. The system learns from authentication outcomes and behavioral patterns, refining its ability to distinguish authentic entities from hackers over time, thereby improving both security reliability and measurement precision
2Reliability
If multiple encryption layers and decentralized validation are implemented, then data protection is strengthened, but system complexity increases significantly
Solution Approach 1:
The patent divides the security system into three distinct modular components: application layer, data protection tool, and data store. Each component handles specific encryption and validation tasks independently, allowing the complex multi-layer encryption system to be managed through clear separation of responsibilities while maintaining strong data protection
Solution Approach 2:
The data protection tool serves as an intermediary between the application and data store, managing the complex encryption operations and trust score calculations. This mediator absorbs much of the system complexity, shielding the application from intricate cryptographic details while ensuring robust data protection through coordinated multi-layer encryption
3Measurement precision
If behavioral analysis and trust score calculations are performed at multiple levels, then hacker detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent segments behavioral analysis across three hierarchical levels (application, data protection tool, data store), with each level performing focused trust score calculations on specific behavioral aspects. This segmentation allows parallel processing of different behavioral dimensions, improving detection accuracy while minimizing sequential processing delays
Solution Approach 2:
The system performs preliminary behavioral recording and initial trust score calculations at the application level before data access operations proceed. By pre-evaluating user behaviors and establishing baseline trust scores in advance, the system reduces real-time processing requirements during actual data access while maintaining high hacker detection accuracy
Data Source
AI summary
One variation of the method includes: at an application, accessing a data stream, encrypting the data stream, and passing the data stream to a data protection tool; at the data protection tool, encrypting the data stream and passing the data stream to a data store; and at the data store, encrypting the data stream; and storing the data stream. This variation of the method also includes, at the application: receiving a request to access the data stream from a first entity; accessing a set of user attributes representing an authentic user; accessing a set of entity attributes; calculating a first trust score for the entity based on the set of user attributes and the set of entity attributes; and, in response to the first trust score falling below a first threshold trust score, generating a decoy data stream and serving the decoy data stream to the entity.