From Machine Learning to Machine Unlearning: Complying with GDPR's Right to be Forgotten while Maintaining Business Value of Predictive Models

• URL: http://arxiv.org/abs/2411.17126v1
• Date: Tue, 26 Nov 2024 05:42:46 GMT
• Title: From Machine Learning to Machine Unlearning: Complying with GDPR's Right to be Forgotten while Maintaining Business Value of Predictive Models
• Authors: Yuncong Yang, Xiao Han, Yidong Chai, Reza Ebrahimi, Rouzbeh Behnia, Balaji Padmanabhan,
• Abstract summary: This work develops a holistic machine learning-to-unlearning framework, called Ensemble-based iTerative Information Distillation (ETID) ETID incorporates a new ensemble learning method to build an accurate predictive model that can facilitate handling data erasure requests. We also introduce an innovative distillation-based unlearning method tailored to the constructed ensemble model to enable efficient and effective data erasure.
• Score: 9.380866972744633
• License:
• Abstract: Recent privacy regulations (e.g., GDPR) grant data subjects the `Right to Be Forgotten' (RTBF) and mandate companies to fulfill data erasure requests from data subjects. However, companies encounter great challenges in complying with the RTBF regulations, particularly when asked to erase specific training data from their well-trained predictive models. While researchers have introduced machine unlearning methods aimed at fast data erasure, these approaches often overlook maintaining model performance (e.g., accuracy), which can lead to financial losses and non-compliance with RTBF obligations. This work develops a holistic machine learning-to-unlearning framework, called Ensemble-based iTerative Information Distillation (ETID), to achieve efficient data erasure while preserving the business value of predictive models. ETID incorporates a new ensemble learning method to build an accurate predictive model that can facilitate handling data erasure requests. ETID also introduces an innovative distillation-based unlearning method tailored to the constructed ensemble model to enable efficient and effective data erasure. Extensive experiments demonstrate that ETID outperforms various state-of-the-art methods and can deliver high-quality unlearned models with efficiency. We also highlight ETID's potential as a crucial tool for fostering a legitimate and thriving market for data and predictive services.

Related papers

• A Scalable Approach to Covariate and Concept Drift Management via Adaptive Data Segmentation [0.562479170374811]
  In many real-world applications, continuous machine learning (ML) systems are crucial but prone to data drift. Traditional drift adaptation methods typically update models using ensemble techniques, often discarding drifted historical data. We contend that explicitly incorporating drifted data into the model training process significantly enhances model accuracy and robustness.
  arXiv  Detail & Related papers  (2024-11-23T17:35:23Z)
• Retrieval Instead of Fine-tuning: A Retrieval-based Parameter Ensemble for Zero-shot Learning [22.748835458594744]
  We introduce Retrieval-based. Ensemble (RPE), a new method that creates a vectorized database of. Low-Rank Adaptations (LoRAs) RPE minimizes the need for extensive training and eliminates the requirement for labeled data, making it particularly effective for zero-shot learning. RPE is well-suited for privacy-sensitive domains like healthcare, as it modifies model parameters without accessing raw data.
  arXiv  Detail & Related papers  (2024-10-13T16:28:38Z)
• Towards a Theoretical Understanding of Memorization in Diffusion Models [76.85077961718875]
  Diffusion probabilistic models (DPMs) are being employed as mainstream models for Generative Artificial Intelligence (GenAI) We provide a theoretical understanding of memorization in both conditional and unconditional DPMs under the assumption of model convergence. We propose a novel data extraction method named textbfSurrogate condItional Data Extraction (SIDE) that leverages a time-dependent classifier trained on the generated data as a surrogate condition to extract training data from unconditional DPMs.
  arXiv  Detail & Related papers  (2024-10-03T13:17:06Z)
• Learn while Unlearn: An Iterative Unlearning Framework for Generative Language Models [49.043599241803825]
  Iterative Contrastive Unlearning (ICU) framework consists of three core components. A Knowledge Unlearning Induction module removes specific knowledge through an unlearning loss. A Contrastive Learning Enhancement module to preserve the model's expressive capabilities against the pure unlearning goal. And an Iterative Unlearning Refinement module that dynamically assess the unlearning extent on specific data pieces and make iterative update.
  arXiv  Detail & Related papers  (2024-07-25T07:09:35Z)
• Exploring Fairness in Educational Data Mining in the Context of the Right to be Forgotten [16.03102654663785]
  In education data mining (EDM) communities, machine learning has achieved remarkable success in discovering patterns and structures to tackle educational challenges. With the increasing demand for the right to be forgotten, there is a growing need for machine learning models to forget sensitive data and its impact. We introduce a novel class of selective forgetting attacks designed to compromise the fairness of learning models while maintaining their predictive accuracy.
  arXiv  Detail & Related papers  (2024-05-27T03:35:50Z)
• Incremental Self-training for Semi-supervised Learning [56.57057576885672]
  IST is simple yet effective and fits existing self-training-based semi-supervised learning methods. We verify the proposed IST on five datasets and two types of backbone, effectively improving the recognition accuracy and learning speed.
  arXiv  Detail & Related papers  (2024-04-14T05:02:00Z)
• The Frontier of Data Erasure: Machine Unlearning for Large Language Models [56.26002631481726]
  Large Language Models (LLMs) are foundational to AI advancements. LLMs pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information. Machine unlearning emerges as a cutting-edge solution to mitigate these concerns.
  arXiv  Detail & Related papers  (2024-03-23T09:26:15Z)
• Federated Learning with Projected Trajectory Regularization [65.6266768678291]
  Federated learning enables joint training of machine learning models from distributed clients without sharing their local data. One key challenge in federated learning is to handle non-identically distributed data across the clients. We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
  arXiv  Detail & Related papers  (2023-12-22T02:12:08Z)
• Fair Machine Unlearning: Data Removal while Mitigating Disparities [5.724350004671127]
  Right to be forgotten is core principle outlined by EU's General Regulation. "Forgetting" can be naively achieved by retraining on remaining data. "Unlearning" impacts other properties critical to real-world applications such as fairness.
  arXiv  Detail & Related papers  (2023-07-27T10:26:46Z)
• Striving for data-model efficiency: Identifying data externalities on group performance [75.17591306911015]
  Building trustworthy, effective, and responsible machine learning systems hinges on understanding how differences in training data and modeling decisions interact to impact predictive performance. We focus on a particular type of data-model inefficiency, in which adding training data from some sources can actually lower performance evaluated on key sub-groups of the population. Our results indicate that data-efficiency is a key component of both accurate and trustworthy machine learning.
  arXiv  Detail & Related papers  (2022-11-11T16:48:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.