Bipartite Graph Construction from Coalesced Features for Privacy Protection
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Solution Overview
Problem
Privacy protected datasets obscure user identifiable information, making it challenging for data analysts to classify features related to users or items, as privacy protection algorithms transform user and item features into bundles, preventing identification by third-party analysts.
Innovation Solution
Constructing bipartite graphs from coalesced features in privacy protected datasets, where user features, item features, and edge features are associated as nodes and edges, enabling graph neural networks to analyze and predict probabilities, improving recommendation system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If privacy protection algorithms are applied to transform user and item features, then user identifiable information is protected, but feature classification capability is lost
Solution Approach 1:
The patent introduces graph neural networks as an intermediary computational model that operates on privacy-protected coalesced features. The GNN learns to classify features into user, item, and edge categories without directly accessing identifiable information, thereby mediating between privacy protection requirements and feature classification needs
Solution Approach 2:
The patent transforms the classification problem from direct feature analysis to graph-structured representation learning. By changing the parameter space from raw features to graph node/edge representations, the system maintains classification capability while operating on privacy-protected data
2Reliability
If coalesced features are used in privacy protected datasets, then privacy is protected, but analyst ability to identify user and item features is reduced
Solution Approach 1:
Graph neural networks serve as an intermediary that bridges the gap between privacy-protected coalesced features and analytical needs. The GNN automatically learns feature relationships and classifications without requiring analysts to directly identify or interpret individual coalesced features
Solution Approach 2:
The patent segments the coalesced features into distinct graph components (user nodes, item nodes, and interaction edges) through automated learning. This segmentation restores analytical utility by organizing features into meaningful categories without exposing identifiable information
3Measurement precision
If graph neural networks are used to analyze bipartite graphs, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The graph neural network performs self-service by automatically learning the graph structure, node classifications, and relationship patterns from the data. This automation reduces the need for manual system configuration and complex preprocessing, offsetting the inherent complexity of GNN implementation
Solution Approach 2:
The system performs preliminary actions by pre-processing data into graph format and pre-training the GNN model on the bipartite structure. This preliminary setup enables accurate predictions while managing complexity through structured data preparation rather than complex runtime processing
Data Source
AI summary
Systems, apparatus, articles of manufacture, and methods are disclosed that include interface circuitry, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to associate first datapoints of a first feature with a first node, associate second datapoints of a second feature with a second node, construct a graph from the first datapoints and the second datapoints, and perform a comparison of a graph accuracy with a baseline accuracy.