Addressing the Challenges of Selective Classification under Differential Privacy: An Empirical Study

In machine learning, differential privacy (DP) and selective classification (SC) are essential to protect sensitive data. DP adds noise to preserve data usefulness while protecting individual privacy, while SC improves reliability by allowing models to refrain from making predictions in cases of uncertainty. This intersection is essential to ensure model accuracy and reliability in privacy-sensitive applications such as healthcare and finance.

There are several major challenges, each of which poses a significant obstacle to maintaining model accuracy and reliability under privacy constraints. It is difficult to prevent a model from becoming overconfident and making mistakes at the same time. Adding DP to protect data makes maintaining model accuracy even more difficult due to the added randomness. Some common methods of SC leak more personal information when using DP. DP also often reduces model behavior, especially for small groups in the data. It also reduces the effectiveness of SC in determining when not to predict when the model is uncertain. Finally, current methods of measuring SC behavior do not compare well across different levels of privacy protection.

To overcome the above challenges, paper Published at the prestigious NeurIPS, the paper proposes a new solution at the intersection of DP and SC. SC is a machine learning technique that allows a model to choose not to predict when it is not confident enough, helping to avoid the possibility of false guesses. The paper addresses the problem of ML models' predictive performance deteriorating with the addition of DP. By conducting a thorough empirical investigation, the authors identify shortcomings of existing selective classification approaches under DP constraints. The paper introduces a novel technique that leverages intermediate model checkpointing to mitigate privacy leakage while maintaining competitive performance. Furthermore, the paper presents a new evaluation metric that allows for a fair comparison of selective classification methods across different privacy levels, addressing the limitations of existing evaluation schemes.

Specifically, the authors proposed Selective Classification by Training Dynamics Ensembles (SCTD), which represents a departure from traditional ensemble methods in the context of DP and SC. Unlike traditional ensemble methods in DP, where configuration increases privacy costs, SCTD leverages intermediate model predictions obtained during the training process to build an ensemble. This novel approach analyzes discrepancies between these intermediate predictions to identify anomalous data points and subsequently reject them. By relying on these intermediate checkpoints rather than building multiple models from scratch, SCTD maintains the original DP guarantees and improves prediction accuracy. This is a significant departure from traditional ensemble methods, which are invalidated in DP due to the increased privacy costs associated with configuration. Essentially, SCTD introduces a post-processing step that exploits the inherent diversity among intermediate models to identify and mitigate privacy risks without compromising prediction performance. This methodological shift enables SCTD to effectively address the challenges posed by DP while increasing the reliability and trustworthiness of selective classifiers.

Furthermore, the authors propose a novel metric to compute a precision-normalized selective classification score by comparing the achieved performance to an upper bound determined by the baseline accuracy and coverage. This score provides a fair evaluation framework, addresses limitations of previous schemes, and enables robust comparison of SC methods under differential privacy constraints.

The researchers conducted a thorough experimental evaluation to evaluate the performance of the SCTD method. They compared SCTD to other selective classification methods across a range of datasets and privacy levels from non-private (ε = ∞) to ε = 1. The experiments included additional entropy normalization and were repeated with five random seeds to obtain statistical significance. The evaluation focused on metrics such as the accuracy vs. coverage tradeoff, recovery of non-private utility by reducing coverage, distance to an accuracy-dependent upper bound, and comparison with a parallel configuration using partitioned ensembles. The evaluation provided valuable insights into the effectiveness of SCTD in DP and its impact on selective classification tasks.

In conclusion, this paper explores the complexities of selective classification under differential privacy constraints and presents empirical evidence and a new scoring method to evaluate performance. The authors find that while the task is inherently difficult, the SCTD method offers a promising trade-off between selective classification accuracy and privacy budget. However, further theoretical analysis is needed, and future research should explore the impact on fairness and strategies to reconcile privacy and subgroup fairness.

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