Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten

Abstract

As the use of machine learning (ML) models is becoming increasingly popular in many real-world applications, there are practical challenges that need to be addressed for model maintenance. One such challenge is to 'undo' the effect of a specific subset of dataset used for training a model. This specific subset may contain malicious or adversarial data injected by an attacker, which affects the model performance. Another reason may be the need for a service provider to remove data pertaining to a specific user to respect the user's privacy. In both cases, the problem is to 'unlearn' a specific subset of the training data from a trained model without incurring the costly procedure of retraining the whole model from scratch. Towards this goal, this paper presents a Markov chain Monte Carlo-based machine unlearning (MCU) algorithm. MCU helps to effectively and efficiently unlearn a trained model from subsets of training dataset. Furthermore, we show that with MCU, we are able to explain the effect of a subset of a training dataset on the model prediction. Thus, MCU is useful for examining subsets of data to identify the adversarial data to be removed. Similarly, MCU can be used to erase the lineage of a user's personal data from trained ML models, thus upholding a user's "right to be forgotten". We empirically evaluate the performance of our proposed MCU algorithm on real-world phishing and diabetes datasets. Results show that MCU can achieve a desirable performance by efficiently removing the effect of a subset of training dataset and outperform an existing algorithm that utilizes the remaining dataset.

Fast and Accurate SNN Model Strengthening for Industrial Applications

“…Brophy [23] discussed a random forest algorithm to support model retraining with a subtree. Nguyen [24] presented a Markov chain Monte Carlo algorithm for extracting data samples to estimate the posterior belief of model parameters. However, these solutions have high resource consumption due to the malicious data distribution and historical training information, making them difficult to use in a generic case.…”

Audit to Forget: A Unified Method to Revoke Patients’ Private Data in Intelligent Healthcare

“…Once the data of certain patients is confirmed to be used to train the target DL model by auditing, forgetting requires the removal of learnt information of certain patients' data from the target DL model, which is also called machine unlearning, while auditing could act as the verification of machine unlearning [18] In order to achieve forgetting, existing unlearning methods could be classified into three major classes, including model-agnostic methods, model-intrinsic methods and data-driven methods [20]. Model-agnostic methods refer to algorithms or frameworks that can be used for different DL models, including differential privacy [18], [21], [22], certified removal [23], [24], [25], statistical query learning [6], decremental learning [26], knowledge adaptation [27], [28] and parameter sampling [29]. Model-intrinsic approaches are those methods designed for specific types of models, such as for softmax classifiers [30], linear models [31], treebased models [32] and Bayesian models [19].…”

Evaluating Machine Unlearning via Epistemic Uncertainty

“…Several approaches were presented, which apply to different models and come with different assumptions and restrictions. So far, these approaches cover unlearning in decision trees and random forests [3,22], linear models such as logistic regression [1,8,10], neural networks [2,7,8,9,10] and even Markov Chain Monte Carlo [6,19]. While most approaches focus on forgetting 1 in a single model, there also exist works that deal with federated models instead [29].…”