Differentially Private Random Forest via Exponential Split Selection
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Solution Overview
Problem
Existing technologies face challenges in preserving the privacy of training data used to build machine-learned classifiers, particularly in machine learning classification, where private information can be derived from the models without adequate privacy-protection mechanisms.
Innovation Solution
A differentially private security system is implemented, which generates a differentially private random forest classifier by determining the number of decision trees, generating splits based on restricted data, and using an exponential mechanism to select splits, thereby adding noise to protect private information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If differential privacy noise is added to protect training data, then privacy protection is improved, but classification accuracy deteriorates
Solution Approach 1:
The training data is divided into multiple partitions, and separate decision trees are constructed for each partition. This segmentation allows the system to apply differential privacy at the partition level rather than requiring noise addition to the entire dataset, thereby maintaining better classification accuracy while still providing privacy protection. The random forest ensemble then aggregates results from these segmented trees.
Solution Approach 2:
The patent applies differential privacy mechanisms during the training phase before the model is deployed. By incorporating privacy protection in advance during tree construction and split selection, the system avoids the need to add additional noise during inference, thus maintaining better accuracy while ensuring privacy is preserved from the outset.
2Reliability
If access controls and data masking are used to protect personal data, then privacy protection is improved, but analytical utility deteriorates
Solution Approach 1:
The patent introduces a differentially private random forest classifier as an intermediary between the raw training data and the prediction output. This intermediary model allows analytical queries to be executed on the trained model without direct access to the underlying sensitive data, thereby preserving both privacy and analytical utility. The model serves as a mediator that releases only the information necessary for predictions while protecting the original data.
3Reliability
If more noise is added to the statistical database results, then privacy protection is improved, but information release deteriorates
Solution Approach 1:
The patent applies differential privacy locally at the level of individual decision tree constructions and split selections rather than adding uniform noise to all statistical results. This localized approach allows the system to provide privacy protection where needed (during training) while preserving the quality and utility of the final model predictions. Different parts of the training process receive appropriate levels of privacy protection based on their sensitivity.
Data Source
AI summary
A request from a client is received to generate a differentially private random forest classifier trained using a set of restricted data. The differentially private random forest classifier is generated in response to the request. Generating the differentially private random forest classifier includes determining a number of decision trees and generating the determined number of decision trees. Generating a decision tree includes generating a set of splits based on the restricted data, determining an information gain for each split, selecting a split from the set using an exponential mechanism, and adding the split to the decision tree. The differentially private random forest classifier is provided to the client.