Anonymized Feature Engineering Pipeline for Privacy-Preserving Recommendations
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Solution Overview
Problem
Recommendation systems face challenges in achieving accurate predictions while preserving user privacy, as they often rely on sensitive data that raises concerns about data security and compliance with privacy regulations like GDPR.
Innovation Solution
A comprehensive feature engineering pipeline is introduced for building recommendation systems using privacy-preserved datasets, which processes data anonymously to enhance feature selection and classification, employing methods like data usage type analysis, anonymous feature classification, and enhanced feature engineering techniques such as numerical feature discretization and count encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensitive user data is used for training recommendation systems, then prediction accuracy is improved, but user privacy and data security are compromised
Solution Approach 1:
The patent introduces an intermediary processing layer that transforms sensitive user data into anonymized features through techniques such as differential privacy, federated learning, and data masking. This intermediary layer enables the recommendation system to access useful patterns while preventing direct exposure of personal information, thus resolving the contradiction between accuracy and privacy protection
Solution Approach 2:
The patent applies parameter changes by transforming the original sensitive data into modified representations with altered statistical properties. Through differential privacy noise injection, feature aggregation, and dimensionality reduction, the data parameters are changed to preserve utility for predictions while removing identifiable information that could compromise user privacy
2Object-affected harmful factors
If data anonymization techniques are applied, then user privacy is preserved, but data learnability and model training effectiveness are reduced
Solution Approach 1:
The patent applies partial anonymization where only specific sensitive fields are obscured while retaining other useful features in their original form. This selective approach ensures that privacy-critical information is protected while sufficient signal remains for effective model training, preventing complete loss of data learnability
Solution Approach 2:
The patent performs preliminary data processing and feature engineering before anonymization to extract and preserve the most informative patterns. By pre-computing aggregated statistics, embedding representations, and feature interactions, the system ensures that essential learnable information is captured before privacy-preserving transformations are applied
3Measurement precision
If comprehensive feature engineering is performed on anonymized data, then feature selection quality is improved, but processing complexity and computational cost increase
Solution Approach 1:
The patent segments the feature engineering process into distinct modular stages: data preprocessing, feature extraction, anonymization, feature selection, and model training. Each module handles specific tasks independently, allowing for optimized processing at each stage and reducing overall system complexity while maintaining feature selection quality
Data Source
AI summary
Systems, apparatus, articles of manufacture, and methods are disclosed to preserve privacy in a user dataset including interface circuitry, machine readable instructions, and programmable circuitry to determine a data usage type for each one of a plurality of user data features in a first dataset, classify the data usage type associated with each user data feature of the plurality of user data feature into a feature category, apply at least one feature engineering mechanism to feature categories of the data usage types of the plurality of user data features, select, based on application of feature engineering, a subset of the plurality of user data features for a feature selection training model, and output a second dataset based on the subset of the plurality of user data for the feature selection training model, the second dataset to include fewer user data features than the first dataset.