Training Data Integrity Scoring for Poisoning Detection
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Solution Overview
Problem
Existing machine learning models are susceptible to training data poisoning, leading to inaccurate outputs that can compromise the integrity of computing environments and result in security breaches or data theft.
Innovation Solution
A training data poisoning detection engine that analyzes replicated training data for changes in feature values and outliers, computing a score to detect potential poisoning and issue a poison alert, enabling real-time remediation actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models use training data from external sources, then the model's learning capability and adaptability are improved, but the risk of training data poisoning and security breaches increases
Solution Approach 1:
The system performs preliminary analysis of training data integrity before the data is used to train the machine learning model. By computing integrity scores and detecting outliers in advance, the system prevents poisoned data from compromising the model, thus maintaining both adaptability and security.
Solution Approach 2:
The system introduces an intermediary training data poisoning detection engine between the external data sources and the machine learning model. This intermediary analyzes training samples, computes integrity scores, and filters out poisoned data before it reaches the model, resolving the contradiction by adding a protective layer without limiting data sources.
2Measurement precision
If the system performs comprehensive analysis of training data integrity, then the detection accuracy is improved, but the computational time and processing resources increase
Solution Approach 1:
The system computes integrity scores for all training samples but applies detailed outlier analysis selectively based on the integrity score thresholds. By performing comprehensive scoring on all data and detailed analysis only on suspicious samples, the system maintains high detection accuracy while reducing overall processing time.
Solution Approach 2:
The analysis process is segmented into two stages: a first stage computing integrity scores for all training samples using feature counters, and a second stage performing detailed outlier analysis only on samples with suspicious integrity scores. This segmentation enables comprehensive analysis where needed while minimizing overall processing time.
3Reliability
If the system monitors and analyzes all training samples for poisoning, then the security integrity is improved, but the device complexity and computational overhead increase
Solution Approach 1:
The system uses the training data itself to detect poisoning by analyzing feature distributions and computing integrity scores based on the data's own statistical properties. This self-service approach maintains high reliability without requiring complex external verification systems, as the data analyzes itself for anomalies.
Solution Approach 2:
The system monitors changes in feature parameters and integrity scores across training batches to detect poisoning. By tracking parameter changes rather than requiring complex structural analysis, the system maintains high reliability with simpler computational overhead, focusing on statistical parameter variations.
Data Source
AI summary
In some examples, a system receives a plurality of training samples of a training data set for a machine learning model, where each training sample of the plurality of training samples comprises a plurality of features. The system determines quantities of changes made to respective features of the plurality of features, computes a score representing an integrity of the training data set based on the quantities, and detects poisoning of the training data set based on the score.