ML Training Pipeline Security Measurement for Membership Inference
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Solution Overview
Problem
Existing machine learning models lack a principled method to determine the level of security against membership inference attacks, making it difficult to assess and enhance security effectively.
Innovation Solution
A method to empirically measure the level of security of a training pipeline by using a representation of the joint distribution of false positive and false negative rates of membership inference attacks, computing a posterior distribution, and determining a confidence interval for empirical epsilon, which guides secure or unprotected deployment of machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If machine learning models are deployed without security measurements, then deployment simplicity is improved, but security level against membership inference attacks deteriorates
Solution Approach 1:
The patent applies preliminary action by performing security measurements and computing empirical epsilon values before deploying machine learning models. The system pre-assesses the security level of training pipelines and determines appropriate deployment protections in advance, rather than adding security measures after deployment issues arise.
Solution Approach 2:
The patent introduces an intermediary security measurement system that acts as a mediator between the training pipeline and deployment process. This intermediary component computes empirical epsilon values and provides security assessments that guide deployment decisions, bridging the gap between model training and secure deployment.
2Reliability
If security measurements and empirical epsilon computations are implemented, then security level is improved, but computational complexity and time consumption increase
Solution Approach 1:
The patent applies self-service by designing a training pipeline that inherently generates the data needed for security measurements during its normal operation. The pipeline uses its own training data and model outputs to compute empirical epsilon values, eliminating the need for separate external security assessment processes and reducing overall measurement time.
3Measurement precision
If confidence interval computations are performed to assess security levels, then measurement precision is improved, but computational complexity increases
Solution Approach 1:
The patent applies parameter changes by computing empirical epsilon values and their confidence intervals through statistical analysis of training pipeline outputs. By transforming the security assessment problem into parameter estimation (computing mean and variance of empirical epsilon), the system achieves precise measurements while managing computational complexity through efficient statistical methods.
Data Source
AI summary
In various examples there is a method of empirically measuring a level of security’ of a training pipeline. The training pipeline is configured to train machine learning models using confidential training data. The method comprises storing a representation of a joint distribution of false positive rate and false negative rate of membership inference attacks on a plurality of machine learning models trained using the training pipeline. The method uses the representation to compute a posterior distribution of the level of security’ from observations of the membership inference attack on the plurality’ of machine learning models trained using the training pipelines. A confidence interval of the level of security is computed from the posterior distribution and the confidence interval is stored.