Data to 7 Oct 2026

Algorithms that Remember: Model Inversion Attacks and Data Protection Law

arXiv 1807.04644 ↗First posted 12 Jul 2018cs.LG cs.CR cs.CYcited by 52

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Cited by 21 in LLM safety and security, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Many individuals are concerned about the governance of machine learning systems and the prevention of algorithmic harms. The EU's recent General Data Protection Regulation (GDPR) has been seen as a core tool for achieving better governance of this area. While the GDPR does apply to the use of models in some limited situations, most of its provisions relate to the governance of personal data, while models have traditionally been seen as intellectual property. We present recent work from the information security literature around `model inversion' and `membership inference' attacks, which indicate that the process of turning training data into machine learned systems is not one-way, and demonstrate how this could lead some models to be legally classified as personal data. Taking this as a probing experiment, we explore the different rights and obligations this would trigger and their utility, and posit future directions for algorithmic governance and regulation.

Key facts. Cited by 52 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jul 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 12; 2022: 12; 2023: 14; 2024: 5; 2025: 6; 2026: 3.

52 papers.

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2021Membership Inference Attacks on Machine Learning: A Surveycited by 306
2022A Survey of Machine Unlearningcited by 298
2021Enhanced Membership Inference Attacks against Machine Learning Modelscited by 237
2021Adaptive Machine Unlearningcited by 164
2021Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Modelscited by 106
2023Regulating ChatGPT and other Large Generative AI Modelscited by 102
2021Demystifying the Draft EU Artificial Intelligence Actcited by 82
2022Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Modelscited by 52
2021Hard to Forget: Poisoning Attacks on Certified Machine Unlearningcited by 47
2021Survey: Leakage and Privacy at Inference Timecited by 44
2023Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomycited by 44
2022Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queriescited by 36

38 more

2022Unlearning Graph Classifiers with Limited Data Resourcescited by 31
2024Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defensescited by 30
2023A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applicationscited by 22
2022Machine Unlearning of Federated Clusterscited by 21
2024Generative AI in EU Law: Liability, Privacy, Intellectual Property, and Cybersecuritycited by 21
2021Property Inference Attacks on Convolutional Neural Networks: Influence and Implications of Target Model's Complexitycited by 19
2022Deletion Inference, Reconstruction, and Compliance in Machine (Un)Learningcited by 19
2023Machine Unlearning: A Surveycited by 18
2022Poisoning Attacks and Defenses on Artificial Intelligence: A Surveycited by 15
2023SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detectioncited by 9
2023BrainWash: A Poisoning Attack to Forget in Continual Learningcited by 9
2023Moderating Model Marketplaces: Platform Governance Puzzles for AI Intermediariescited by 9
2021Correlation inference attacks against machine learning modelscited by 7
2022Why we need biased AI -- How including cognitive and ethical machine biases can enhance AI systemscited by 7
2021Knowledge Cross-Distillation for Membership Privacycited by 6
2022Desiderata for next generation of ML model servingcited by 5
2022GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs)cited by 4
2023SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier Detectioncited by 4
2023Transpose Attack: Stealing Datasets with Bidirectional Trainingcited by 4
2025Machine Learners Should Acknowledge the Legal Implications of Large Language Models as Personal Datacited by 4
2023PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clientscited by 3
2023The Ethics of AI Value Chainscited by 3
2024WPN: An Unlearning Method Based on N-pair Contrastive Learning in Language Modelscited by 3
2025Scrub It Out! Erasing Sensitive Memorization in Code Language Models via Machine Unlearningcited by 3
2026Safety, Security, and Cognitive Risks in World Modelscited by 3
2023CovarNav: Machine Unlearning via Model Inversion and Covariance Navigationcited by 2
2024Discriminative Adversarial Unlearningcited by 2
2025A hierarchical approach for assessing the vulnerability of tree-based classification models to membership inference attackcited by 2
2025Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Modelscited by 2
2025Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Reviewcited by 2
2021Machine Learning Models Disclosure from Trusted Research Environments (TRE), Challenges and Opportunitiescited by 1
2022LDL: A Defense for Label-Based Membership Inference Attackscited by 1
2023SoK: Memorisation in machine learningcited by 1
2024Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquirycited by 1
2025Towards integration of Privacy Enhancing Technologies in Explainable Artificial Intelligencecited by 1
2026Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspectivecited by 1
2026Explanations Leak: Membership Inference with Differential Privacy and Active Learning Defensecited by 1
2021Adversarial Machine Learning for Cybersecurity and Computer Vision: Current Developments and Challengescited by 0

50 most cited of 52 All 52 (JSON)