Distributed Inference Model Management for Compromise Detection
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Solution Overview
Problem
Computing devices face challenges in managing inference models due to limited resources and potential security threats from compromised data processing systems, which can impair accurate inference generation.
Innovation Solution
A system and method for managing inference models by evaluating processing results across multiple data processing systems, initiating self-healing processes for compromised systems, and rebalancing resources to maintain accurate inference generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data processing systems are deployed to host distributed inference models, then service availability and processing capacity are improved, but the risk of system compromise and security threats increases
Solution Approach 1:
The patent implements a feedback mechanism where processing results from multiple data processing systems are continuously monitored and compared. When a compromise is detected through result comparison, the system automatically responds by initiating self-healing processes or removing compromised systems, creating a closed-loop security management system that adapts to threats in real-time
Solution Approach 2:
The patent introduces an intermediary verification layer that compares processing results across multiple systems before accepting inference outputs. This intermediary comparison mechanism acts as a mediator to detect compromised systems without requiring direct security scanning, allowing the system to maintain productivity while filtering out malicious or erroneous results
2Reliability
If active scanning and security monitoring are implemented, then system compromise detection is improved, but computing resource consumption increases
Solution Approach 1:
The patent employs self-service security monitoring where each data processing system contributes its own processing results to the collective verification process. Instead of requiring external security scanning resources, the systems use their normal operational outputs to mutually verify each other's integrity, turning operational data into security verification data without additional resource overhead
Solution Approach 2:
The patent creates virtual copies of processing results for comparison purposes. Rather than duplicating expensive security scanning operations, the system copies and compares the natural output results from multiple systems, using result replication instead of operational replication to detect compromises efficiently
3Productivity
If multiple data processing systems are used for distributed inference, then processing capacity is improved, but the complexity of managing and monitoring these systems increases
Solution Approach 1:
The patent merges security monitoring, result verification, and system management functions into a unified comparison process. By combining these previously separate functions into a single result-comparison operation, the system reduces management complexity while maintaining distributed processing capacity, as the same data flow serves multiple purposes
4Duration of action of stationary object
If compromised systems are not removed, then service continuity is maintained, but inference accuracy and reliability deteriorate
Solution Approach 1:
The patent implements dynamic system configuration where the composition of data processing systems is not fixed but can change in response to detected compromises. The system dynamically adjusts which systems are active and trusted based on real-time result comparison, allowing service continuity through automatic reconfiguration rather than static system assignments
Data Source
AI summary
Methods and systems for managing inference models hosted by data processing systems are disclosed. To manage the inference models, data processing systems that host the inference models may be monitored for risk of compromise. The data processing systems may be monitored by evaluating the processing results generated by the data processing systems. When a data processing system is identified as being compromised, a self-healing process or other remediation process may be initiated to reduce or eliminate the threat to successful inference generation presented by the compromised data processing system.