Anonymizing Network Traffic for AI Model Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current network analysis methods using artificial intelligence models face inaccuracies and resource inefficiencies due to the need for real-time network data processing, which can introduce delays and privacy concerns when handling sensitive information, and summarized data may not provide comprehensive insights.
Innovation Solution
A local monitoring server device receives and anonymizes network traffic data in real-time, generating masked packets that preserve privacy while allowing for accurate AI model training without impacting network performance, thereby enhancing data quality and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time network data is processed by AI models, then network analysis accuracy is improved, but privacy concerns arise due to handling sensitive information
Solution Approach 1:
The patent extracts and removes privacy-related data from network packets before processing them through AI models. The system identifies and strips sensitive information such as IP addresses, user identifiers, and other personally identifiable data, retaining only the network performance metrics needed for analysis. This resolves the contradiction by enabling accurate network analysis while eliminating privacy concerns through selective data extraction.
Solution Approach 2:
The patent introduces an intermediary processing layer between network data collection and AI model processing. This intermediary component anonymizes data by replacing identifying information with pseudonyms or aggregated metrics, allowing the AI model to process network patterns without accessing sensitive personal information. The intermediary preserves analytical accuracy while protecting privacy through data transformation.
2Measurement precision
If comprehensive network data is collected for AI model training, then data quality is improved, but computing resources are consumed
Solution Approach 1:
The patent extracts only the essential network performance metrics from comprehensive network data, discarding redundant and privacy-sensitive information. By selecting only relevant features such as bandwidth utilization, latency, and packet loss rates, the system maintains high data quality for AI training while significantly reducing the computational burden of processing and storing vast amounts of raw network data.
Solution Approach 2:
The patent applies partial action by processing a carefully selected subset of network data that is sufficient for effective AI model training without the need to analyze every packet. The system identifies critical performance indicators and focuses computational resources on these key metrics, achieving high-quality training results with reduced resource consumption by avoiding excessive processing of all available data.
3Object-affected harmful factors
If network data is anonymized, then privacy protection is improved, but data comprehensiveness may be reduced
Solution Approach 1:
The patent creates anonymized copies of network data that preserve the structural and behavioral characteristics needed for analysis while removing identifying information. The system generates synthetic or transformed versions of network packets that maintain performance metric relationships and patterns, enabling comprehensive AI training while ensuring privacy protection through data replication rather than direct processing of original sensitive data.
Data Source
AI summary
A device may receive, from a network device in near-real time, a packet of data associated with network traffic of a network, wherein the packet includes privacy-related data and network-related data. The device may read the privacy-related data from the packet. The device may generate anonymous data based on the privacy-related data, wherein the anonymous data obscures the privacy-related data. The device may generate a mapping between the anonymous data and the privacy-related data. The device may combine the anonymous data and the network-related data to generate a masked packet. The device may provide the masked packet to a server device. The device may receive, from the server device, data identifying a recommendation that is generated by processing the masked packet with an artificial intelligence model. The device may perform one or more actions based on the recommendation.