# A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks

arXiv: https://arxiv.org/abs/2406.06186 · HTML: https://mlsanity.com/paper/2406.06186/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in Machine unlearning, one of 12 directions in LLM safety and security.
- Builds on 95 of its 139 references that are in the map.
- First posted on arXiv 10 Jun 2024.

## Placed in

- LLM safety and security / [Machine unlearning](https://mlsanity.com/safety/machine-unlearning/)

## Builds on (most cited first, 30 of 95)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Gradient Surgery for Multi-Task Learning](https://mlsanity.com/paper/2001.06782/) (2020)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019)
- [The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks](https://mlsanity.com/paper/1802.08232/) (2018)
- [Stealing Machine Learning Models via Prediction APIs](https://mlsanity.com/paper/1609.02943/) (2016)
- [Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks](https://mlsanity.com/paper/1911.04933/) (2019)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Certified Data Removal from Machine Learning Models](https://mlsanity.com/paper/1911.03030/) (2019)
- [Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm](https://mlsanity.com/paper/1608.04471/) (2016)
- [ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models](https://mlsanity.com/paper/1806.01246/) (2018)
- [Making AI Forget You: Data Deletion in Machine Learning](https://mlsanity.com/paper/1907.05012/) (2019)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [Does Learning Require Memorization? A Short Tale about a Long Tail](https://mlsanity.com/paper/1906.05271/) (2019)
- [New insights and perspectives on the natural gradient method](https://mlsanity.com/paper/1412.1193/) (2014)
- [Knowledge Unlearning for Mitigating Privacy Risks in Language Models](https://mlsanity.com/paper/2210.01504/) (2022)
- [Who's Harry Potter? Approximate Unlearning in LLMs](https://mlsanity.com/paper/2310.02238/) (2023)
- [Membership Inference Attacks on Machine Learning: A Survey](https://mlsanity.com/paper/2103.07853/) (2021)
- [Amnesiac Machine Learning](https://mlsanity.com/paper/2010.10981/) (2020)
- [Descent-to-Delete: Gradient-Based Methods for Machine Unlearning](https://mlsanity.com/paper/2007.02923/) (2020)

## Built on by (most cited first, 15 of 15)

- [Don't Forget Too Much: Towards Machine Unlearning on Feature Level](https://mlsanity.com/paper/2406.10951/) (2024)
- [Bias Amplification in RAG: Poisoning Knowledge Retrieval to Steer LLMs](https://mlsanity.com/paper/2506.11415/) (2025)
- [Towards Reliable Forgetting: A Survey on Machine Unlearning Verification](https://mlsanity.com/paper/2506.15115/) (2025)
- [A Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questions](https://mlsanity.com/paper/2412.12836/) (2024)
- [Unilogit: Robust Machine Unlearning for LLMs Using Uniform-Target Self-Distillation](https://mlsanity.com/paper/2505.06027/) (2025)
- [Instance-Level Difficulty: A Missing Perspective in Machine Unlearning](https://mlsanity.com/paper/2410.03043/) (2024)
- [Efficient Machine Unlearning by Model Splitting and Core Sample Selection](https://mlsanity.com/paper/2505.07026/) (2025)
- [Game-Theoretic Machine Unlearning: Mitigating Extra Privacy Leakage](https://mlsanity.com/paper/2411.03914/) (2024)
- [Targeted Unlearning Using Perturbed Sign Gradient Methods With Applications On Medical Images](https://mlsanity.com/paper/2505.21872/) (2025)
- [Structural Disentanglement in Bilinear MLPs via Architectural Inductive Bias](https://mlsanity.com/paper/2602.05635/) (2026)
- [Creative Ownership in the Age of AI](https://mlsanity.com/paper/2602.12270/) (2026)
- [Relationship-Aware Safety Unlearning for Multimodal LLMs](https://mlsanity.com/paper/2603.14185/) (2026)
- [DG-CoLearn: An Efficient Collaborative Learning Framework for Dynamic Graphs](https://mlsanity.com/paper/2605.31427/) (2026)
- [Physics-Aware Machine Unlearning for Cyber-Physical Systems](https://mlsanity.com/paper/2609.36633/) (2026)
- [Weeding Out Bad Seeds: Initial-Noise-Robust Unlearning for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2609.37537/) (2026)

## Abstract (arXiv)

The explosive growth of machine learning has made it a critical infrastructure in the era of artificial intelligence. The extensive use of data poses a significant threat to individual privacy. Various countries have implemented corresponding laws, such as GDPR, to protect individuals' data privacy and the right to be forgotten. This has made machine unlearning a research hotspot in the field of privacy protection in recent years, with the aim of efficiently removing the contribution and impact of individual data from trained models. The research in academia on machine unlearning has continuously enriched its theoretical foundation, and many methods have been proposed, targeting different data removal requests in various application scenarios. However, recently researchers have found potential privacy leakages of various of machine unlearning approaches, making the privacy preservation on machine unlearning area a critical topic. This paper provides an overview and analysis of the existing research on machine unlearning, aiming to present the current vulnerabilities of machine unlearning approaches. We analyze privacy risks in various aspects, including definitions, implementation methods, and real-world applications. Compared to existing reviews, we analyze the new challenges posed by the latest malicious attack techniques on machine unlearning from the perspective of privacy threats. We hope that this survey can provide an initial but comprehensive discussion on this new emerging area.