Poisoned Data Management in Service Systems
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Solution Overview
Problem
In service-to-service systems, corrupted data can propagate and cause widespread contamination, leading to significant downtime and the need for extensive system rollbacks, as existing technologies lack effective mechanisms for identifying and correcting corrupted data.
Innovation Solution
A system with a message broker and message consumer architecture that tracks data sourcing through data graphs, allowing for the identification and remediation of corrupted data, ensuring that corrupted data is corrected at its source and propagated corrections are made across affected data elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is allowed to flow freely between services in a service-to-service system, then system productivity and service responsiveness are improved, but corrupted data can propagate uncontrollably causing widespread contamination and system downtime
Solution Approach 1:
The patent introduces an intermediary mechanism (data provenance tracking system) that mediates data flow between services. This system tracks the origin and transformation of data through data lineage graphs, enabling identification of corrupted data sources without blocking legitimate data flow. The intermediary allows continuous service operation while providing the capability to detect and contain data corruption issues.
Solution Approach 2:
The patent implements feedback mechanisms where data quality metrics and provenance information are continuously monitored and fed back to the system. When corrupted data is detected, the feedback loop enables automatic containment actions such as isolating affected data streams or triggering remediation processes, thus maintaining system reliability while preserving overall productivity.
2Loss of time
If corrupted data is not actively tracked and corrected, then system operation continues uninterrupted, but extensive system rollbacks are required when data corruption spreads
Solution Approach 1:
The patent applies preliminary action by establishing data provenance tracking infrastructure before data corruption occurs. Data lineage graphs are constructed in advance, recording the origin and transformation path of each data element. This pre-established tracking system enables rapid identification and containment of corrupted data, preventing widespread contamination and eliminating the need for extensive system rollbacks.
Solution Approach 2:
The patent segments the data system into traceable units with defined lineage relationships. By dividing data flow into discrete, trackable segments represented in data lineage graphs, the system can isolate and address corruption issues in specific segments without affecting the entire system, thereby minimizing downtime without requiring proportionally complex infrastructure.
3Measurement precision
If data lineage tracking is implemented across the entire system, then corrupted data can be identified and corrected at its source, but system complexity and computational overhead increase
Solution Approach 1:
The patent applies local quality by implementing data lineage tracking with varying levels of detail appropriate to different data contexts. Critical data elements receive more detailed provenance tracking while less critical data uses simplified tracking. This selective approach maintains high identification accuracy for important data while reducing overall system complexity and computational overhead.
Solution Approach 2:
The patent implements partial action by focusing data lineage tracking on critical data flows and high-risk operations rather than uniformly tracking all data. This selective tracking provides sufficient precision for identifying corrupted data in critical systems while avoiding the excessive complexity that would result from comprehensive tracking of every data element throughout the entire system.
Data Source
AI summary
A system for poisoned data management includes an interface and a processor. The interface is configured to receive an indication of poisoned data in a published event. The processor is configured to mark the poisoned data in a data graph; mark in the data graph a set of downstream nodes as poisoned; and store the data graph.