Differentially Private Pool-Based Active Learning System
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Solution Overview
Problem
Existing machine learning technologies face challenges in achieving differential privacy in active learning settings, particularly when labeled data is scarce or expensive, and in federated learning scenarios where privacy cannot be guaranteed, as traditional differentially private mechanisms are primarily designed for non-interactive supervised learning.
Innovation Solution
A system that facilitates differential privacy in pool-based active learning by selecting unlabeled samples using randomized trials and updating classifiers with informative and non-informative samples, employing concepts like version spaces, Bernoulli distributions, and mechanisms such as output perturbation and exponential mechanisms to ensure privacy-aware selection and update processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional differentially private mechanisms are applied to non-interactive supervised learning, then privacy protection is improved, but label efficiency deteriorates due to the abundance of labeled data requirement
Solution Approach 1:
The system dynamically adapts the sampling probability for each unlabeled sample based on its informativeness score, rather than using a fixed sampling strategy. This allows the system to efficiently select the most informative samples for labeling while maintaining differential privacy, thereby reducing the total number of labeled data points needed compared to traditional approaches.
Solution Approach 2:
The system changes the sampling parameters adaptively by computing informativeness scores and using these scores to determine sampling probabilities. This parameter change enables the system to focus on high-value samples, improving label efficiency while preserving privacy guarantees through controlled noise addition based on the scoring mechanism.
2Quantity of substance
If active learning selects only informative samples for labeling, then label efficiency is improved, but privacy protection deteriorates due to targeted selection revealing information
Solution Approach 1:
The system introduces an intermediary randomness mechanism (Bernoulli sampling) between the informativeness scoring and actual sample selection. This intermediary layer ensures that even highly informative samples are not always selected, adding privacy protection while still achieving label efficiency through probabilistic selection based on informativeness scores.
Solution Approach 2:
The system uses parameter changes by adjusting sampling probabilities based on informativeness scores rather than deterministic selection. This probabilistic approach with configurable parameters allows the system to balance between selecting informative samples for efficiency and introducing randomness for privacy protection.
3Reliability
If randomized sampling is used to protect privacy, then privacy protection is improved, but sample selection efficiency deteriorates due to inclusion of non-informative samples
Solution Approach 1:
The system applies local quality by treating each sample differently based on its individual informativeness score. Rather than uniform random sampling, each sample receives a customized sampling probability proportional to its informativeness, ensuring that high-value samples are more likely to be selected while still maintaining privacy through randomness.
Solution Approach 2:
The system changes the sampling parameter from a uniform distribution to a non-uniform distribution weighted by informativeness scores. This parameter change enables the system to maintain privacy through randomized sampling while improving efficiency by biasing the selection toward more informative samples.
Data Source
AI summary
The system determines a version space associated with a set of data comprising a pool of unlabeled samples and a first plurality of labeled samples, wherein the version space includes a first set of classifiers corresponding to the first plurality of labeled samples. The system selects, from the pool of unlabeled samples, a second plurality of unlabeled samples comprising informative samples and non-informative samples. A respective informative sample corresponds to a first hyperplane which intersects the version space, and a respective non-informative sample corresponds to a second hyperplane which does not intersect the version space. The system acquires labels corresponding to the second plurality of unlabeled samples to obtain a third plurality of labeled samples. The system updates the first set of classifiers based on the third plurality of labeled samples, thereby improving accuracy of the first set of classifiers.