Network Fingerprinting Models for Privacy-Preserving Metadata Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning technologies face challenges in preserving user privacy and preventing algorithmic bias while personalizing services, particularly in predicting user metadata based on network fingerprinting, due to the need for large data sets that include sensitive information and the potential for discriminatory outcomes.
Innovation Solution
A method and apparatus that utilize client-based fingerprinting to create a device's connection path fingerprint using routing information from probe packets, employing prediction models trained with transformed and partly fabricated data to predict user metadata without exposing sensitive information, using centralized or federated learning to maintain privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning algorithms are trained using large data sets including sensitive user information to improve personalization accuracy, then prediction accuracy is improved, but user privacy is compromised
Solution Approach 1:
The patent introduces network fingerprinting as an intermediary mechanism that captures device characteristics and connection path information without directly accessing sensitive user data. This mediator enables the prediction model to infer user metadata indirectly through routing information and IP address patterns, thereby maintaining prediction accuracy while preserving user privacy
Solution Approach 2:
The patent extracts only the necessary routing information and network topology data from the communication process to create fingerprints, while deliberately excluding sensitive user identifiers and personal information. This selective extraction allows the system to train effective prediction models using only non-sensitive network metadata
2Adaptability or versatility
If routing information and connection path data are collected to create fingerprints for prediction, then personalization capability is improved, but data privacy risks increase
Solution Approach 1:
The system performs self-service by generating network fingerprints automatically from passive observation of routing information during normal device operations. The fingerprinting process utilizes publicly available network topology data and device's own connection patterns without requiring users to voluntarily share additional personal information, thus enhancing personalization while minimizing privacy intrusion
3Measurement precision
If prediction models are trained on centralized data to improve model performance, then prediction performance is improved, but user consent and control are reduced
Solution Approach 1:
The patent segments the training data into distributed network fingerprint records collected from multiple devices independently. Each device contributes anonymized routing information to the centralized training process, allowing the model to learn from diverse network patterns while maintaining user control over local data generation. Users retain the ability to manage their device's network configuration independently
Data Source
AI summary
A method, an apparatus and a computer program product for machine learning based on network fingerprinting, while preserving privacy in generating a prediction model for predicting user metadata. Routing information of a device is obtained based probe packets sent by the device to a server that is connectable to the device via the Internet, such as a series of packet hops implemented to route the packets to the server or a series of Internet Protocol (IP) addresses of the series of packet hops until reaching the Internet. A fingerprint describing an architecture of connection path of the device to the Internet is created based on the routing information. The prediction model is trained using training dataset that includes pairs of fingerprints and labels using edge devices having known labels, that are indicative of a routing information of an edge device to the Internet.