Network Topology Traces with Minimal Data Collection
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Solution Overview
Problem
Network topology traces often reveal sensitive information about network structures, and existing methods struggle to perform effective diagnostics with minimal data collection, especially when only limited information is available, such as round trip time data, which can hinder pinpointing issues without exposing network details.
Innovation Solution
A device in a network receives privatized network trace data comprising round trip time information, groups it into segments, and calculates segment trip time metrics to analyze communication paths while maintaining privacy, using machine learning techniques to determine path characteristics and diagnose potential problems without disclosing sensitive network information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full network trace information is collected, then diagnostic precision is improved, but network privacy is compromised
Solution Approach 1:
The patent extracts only the essential diagnostic information (round trip time) from the trace data while leaving out sensitive network topology information. This allows diagnostics to be performed on communication path performance without revealing the actual network structure, node identities, or routing details that would compromise privacy.
Solution Approach 2:
The patent creates a simplified copy of the network trace data that contains only the necessary performance metrics (round trip time values) rather than the complete trace information. This copied data structure maintains diagnostic utility while eliminating privacy-sensitive elements, enabling analysis without exposing the actual network topology.
2Loss of information
If minimal data is collected for privacy protection, then network privacy is maintained, but diagnostic capability deteriorates
Solution Approach 1:
The patent enables the minimal round trip time data to serve multiple diagnostic functions on its own, without requiring additional network information. By calculating segment trip time metrics from just the RTT values, the system makes the limited data work harder and provide more diagnostic value, compensating for the reduced data volume through sophisticated metric computation.
Solution Approach 2:
The patent transforms the raw round trip time measurements into derived metrics (segment trip time metrics) that provide deeper diagnostic insights. This parameter transformation allows the system to extract more information from the same minimal data set, improving diagnostic capability without collecting additional sensitive network information.
3Measurement precision
If detailed trace information is analyzed, then problem localization accuracy is improved, but data collection complexity increases
Solution Approach 1:
The patent segments the communication path into distinct network segments based on round trip time patterns, allowing problem localization without requiring detailed knowledge of each individual hop. This segmentation approach simplifies data collection by focusing on aggregate segment characteristics rather than individual node information, reducing complexity while maintaining diagnostic accuracy.
Data Source
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AI summary
In one embodiment, a device in a network receives privatized network trace data that comprises round trip time information for hops along a communication path. The device groups the trace data into a plurality of network segments based on the round trip time information. The device calculates a segment trip time metric for one or more of the network segments based on the round trip time information associated with the one or more network segments.