{"total":218,"rows":[{"id":"2112.03570","title":"Membership Inference Attacks From First Principles","created":"2021-12-07","cited_by_count":878,"area":"safety"},{"id":"2103.07853","title":"Membership Inference Attacks on Machine Learning: A Survey","created":"2021-03-14","cited_by_count":321,"area":"safety"},{"id":"2209.02299","title":"A Survey of Machine Unlearning","created":"2022-09-06","cited_by_count":312,"area":"safety"},{"id":"2111.09679","title":"Enhanced Membership Inference Attacks against Machine Learning Models","created":"2021-11-18","cited_by_count":248,"area":"safety"},{"id":"2108.11577","title":"Machine Unlearning of Features and Labels","created":"2021-08-26","cited_by_count":229,"area":"safety"},{"id":"2402.00888","title":"Security and Privacy Challenges of Large Language Models: A Survey","created":"2024-01-30","cited_by_count":186,"area":"safety"},{"id":"2203.03929","title":"Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks","created":"2022-03-08","cited_by_count":175,"area":"safety"},{"id":"2111.08440","title":"On the Importance of Difficulty Calibration in Membership Inference Attacks","created":"2021-11-15","cited_by_count":147,"area":"safety"},{"id":"2103.14991","title":"Graph Unlearning","created":"2021-03-27","cited_by_count":138,"area":"safety"},{"id":"2201.05629","title":"Zero-Shot Machine Unlearning","created":"2022-01-14","cited_by_count":133,"area":"safety"},{"id":"2312.03262","title":"Low-Cost High-Power Membership Inference Attacks","created":"2023-12-06","cited_by_count":107,"area":"safety"},{"id":"2208.14933","title":"Membership Inference Attacks by Exploiting Loss Trajectory","created":"2022-08-31","cited_by_count":101,"area":"safety"},{"id":"2305.06535","title":"KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment","created":"2023-05-11","cited_by_count":99,"area":"safety"},{"id":"2311.06062","title":"Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration","created":"2023-11-10","cited_by_count":98,"area":"safety"},{"id":"2102.02551","title":"ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models","created":"2021-02-04","cited_by_count":85,"area":"safety"},{"id":"2101.01341","title":"Practical Blind Membership Inference Attack via Differential Comparisons","created":"2021-01-05","cited_by_count":78,"area":"safety"},{"id":"2108.11023","title":"EncoderMI: Membership Inference against Pre-trained Encoders in Contrastive Learning","created":"2021-08-25","cited_by_count":73,"area":"safety"},{"id":"2201.09441","title":"Federated Unlearning with Knowledge Distillation","created":"2022-01-24","cited_by_count":73,"area":"safety"},{"id":"2210.00968","title":"Membership Inference Attacks Against Text-to-image Generation Models","created":"2022-10-03","cited_by_count":69,"area":"safety"},{"id":"2110.08324","title":"Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture","created":"2021-10-15","cited_by_count":67,"area":"safety"},{"id":"2301.11578","title":"Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers","created":"2023-01-27","cited_by_count":67,"area":"safety"},{"id":"2307.03694","title":"Scalable Membership Inference Attacks via Quantile Regression","created":"2023-07-07","cited_by_count":65,"area":"safety"},{"id":"2406.17975","title":"SoK: Membership Inference Attacks on LLMs are Rushing Nowhere (and How to Fix It)","created":"2024-06-25","cited_by_count":59,"area":"safety"},{"id":"2109.08045","title":"Membership Inference Attacks Against Recommender Systems","created":"2021-09-16","cited_by_count":57,"area":"safety"},{"id":"2202.03335","title":"Membership Inference Attacks and Defenses in Neural Network Pruning","created":"2022-02-07","cited_by_count":45,"area":"safety"},{"id":"2409.01380","title":"Membership Inference Attacks Against In-Context Learning","created":"2024-09-02","cited_by_count":43,"area":"safety"},{"id":"2210.10750","title":"Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries","created":"2022-10-19","cited_by_count":40,"area":"safety"},{"id":"2404.02062","title":"Digital Forgetting in Large Language Models: A Survey of Unlearning Methods","created":"2024-04-02","cited_by_count":40,"area":"safety"},{"id":"2207.05801","title":"RelaxLoss: Defending Membership Inference Attacks without Losing Utility","created":"2022-07-12","cited_by_count":37,"area":"safety"},{"id":"2502.18943","title":"Towards Label-Only Membership Inference Attack against Pre-trained Large Language Models","created":"2025-02-26","cited_by_count":37,"area":"safety"},{"id":"2102.04140","title":"Quantifying and Mitigating Privacy Risks of Contrastive Learning","created":"2021-02-08","cited_by_count":36,"area":"safety"},{"id":"2106.11384","title":"Membership Inference on Word Embedding and Beyond","created":"2021-06-21","cited_by_count":35,"area":"safety"},{"id":"2209.06997","title":"M^4I: Multi-modal Models Membership Inference","created":"2022-09-15","cited_by_count":34,"area":"safety"},{"id":"2211.00463","title":"Amplifying Membership Exposure via Data Poisoning","created":"2022-11-01","cited_by_count":34,"area":"safety"},{"id":"2309.08230","title":"A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services","created":"2023-09-15","cited_by_count":34,"area":"safety"},{"id":"2111.07608","title":"Property Inference Attacks Against GANs","created":"2021-11-15","cited_by_count":33,"area":"safety"},{"id":"2103.07101","title":"On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models","created":"2021-03-12","cited_by_count":31,"area":"safety"},{"id":"2203.06580","title":"One Parameter Defense -- Defending against Data Inference Attacks via Differential Privacy","created":"2022-03-13","cited_by_count":27,"area":"safety"},{"id":"2305.13257","title":"Watermarking Text Data on Large Language Models for Dataset Copyright","created":"2023-05-22","cited_by_count":27,"area":"safety"},{"id":"2202.03460","title":"Deletion Inference, Reconstruction, and Compliance in Machine (Un)Learning","created":"2022-02-07","cited_by_count":24,"area":"safety"},{"id":"2407.15098","title":"SeqMIA: Sequential-Metric Based Membership Inference Attack","created":"2024-07-21","cited_by_count":23,"area":"safety"},{"id":"2105.02866","title":"Membership Inference Attacks on Deep Regression Models for Neuroimaging","created":"2021-05-06","cited_by_count":22,"area":"safety"},{"id":"2203.02077","title":"User-Level Membership Inference Attack against Metric Embedding Learning","created":"2022-03-04","cited_by_count":22,"area":"safety"},{"id":"2409.00426","title":"Is Difficulty Calibration All We Need? Towards More Practical Membership Inference Attacks","created":"2024-08-31","cited_by_count":21,"area":"safety"},{"id":"2205.13680","title":"Membership Inference Attack Using Self Influence Functions","created":"2022-05-26","cited_by_count":20,"area":"safety"},{"id":"2206.03317","title":"Subject Membership Inference Attacks in Federated Learning","created":"2022-06-07","cited_by_count":20,"area":"safety"},{"id":"2407.10494","title":"Learning to Unlearn for Robust Machine Unlearning","created":"2024-07-15","cited_by_count":19,"area":"safety"},{"id":"2205.14055","title":"A Blessing of Dimensionality in Membership Inference through Regularization","created":"2022-05-27","cited_by_count":18,"area":"safety"},{"id":"2307.01701","title":"Synthetic is all you need: removing the auxiliary data assumption for membership inference attacks against synthetic data","created":"2023-07-04","cited_by_count":18,"area":"safety"},{"id":"2404.12922","title":"Is Retain Set All You Need in Machine Unlearning? Restoring Performance of Unlearned Models with Out-Of-Distribution Images","created":"2024-04-19","cited_by_count":18,"area":"safety"},{"id":"2102.07762","title":"Membership Inference Attacks are Easier on Difficult Problems","created":"2021-02-15","cited_by_count":17,"area":"safety"},{"id":"2206.01349","title":"On the Privacy Properties of GAN-generated Samples","created":"2022-06-03","cited_by_count":17,"area":"safety"},{"id":"2209.08615","title":"Membership Inference Attacks and Generalization: A Causal Perspective","created":"2022-09-18","cited_by_count":17,"area":"safety"},{"id":"2302.12685","title":"Active Membership Inference Attack under Local Differential Privacy in Federated Learning","created":"2023-02-24","cited_by_count":17,"area":"safety"},{"id":"2111.05113","title":"Membership Inference Attacks Against Self-supervised Speech Models","created":"2021-11-09","cited_by_count":16,"area":"safety"},{"id":"2202.10276","title":"Poisoning Attacks and Defenses on Artificial Intelligence: A Survey","created":"2022-02-21","cited_by_count":16,"area":"safety"},{"id":"2208.10445","title":"Membership-Doctor: Comprehensive Assessment of Membership Inference Against Machine Learning Models","created":"2022-08-22","cited_by_count":16,"area":"safety"},{"id":"2202.12232","title":"Bounding Membership Inference","created":"2022-02-24","cited_by_count":15,"area":"safety"},{"id":"2212.00612","title":"Purifier: Defending Data Inference Attacks via Transforming Confidence Scores","created":"2022-12-01","cited_by_count":15,"area":"safety"},{"id":"2310.05847","title":"Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems","created":"2023-10-06","cited_by_count":15,"area":"safety"},{"id":"2311.16136","title":"ERASER: Machine Unlearning in MLaaS via an Inference Serving-Aware Approach","created":"2023-11-03","cited_by_count":15,"area":"safety"},{"id":"2406.06186","title":"A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks","created":"2024-06-10","cited_by_count":15,"area":"safety"},{"id":"2202.01243","title":"Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference","created":"2022-02-02","cited_by_count":14,"area":"safety"},{"id":"2309.07983","title":"SLMIA-SR: Speaker-Level Membership Inference Attacks against Speaker Recognition Systems","created":"2023-09-14","cited_by_count":14,"area":"safety"},{"id":"2310.11397","title":"Last One Standing: A Comparative Analysis of Security and Privacy of Soft Prompt Tuning, LoRA, and In-Context Learning","created":"2023-10-17","cited_by_count":14,"area":"safety"},{"id":"2402.00179","title":"De-identification is not enough: a comparison between de-identified and synthetic clinical notes","created":"2024-01-31","cited_by_count":14,"area":"safety"},{"id":"2312.02052","title":"DUCK: Distance-based Unlearning via Centroid Kinematics","created":"2023-12-04","cited_by_count":13,"area":"safety"},{"id":"2402.09225","title":"Is My Data in Your AI? Membership Inference Test (MINT) applied to Face Biometrics","created":"2024-02-14","cited_by_count":13,"area":"safety"},{"id":"2403.02116","title":"Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks","created":"2024-03-04","cited_by_count":13,"area":"safety"},{"id":"2405.16978","title":"OSLO: One-Shot Label-Only Membership Inference Attacks","created":"2024-05-27","cited_by_count":13,"area":"safety"},{"id":"2206.12401","title":"Debiasing Learning for Membership Inference Attacks Against Recommender Systems","created":"2022-06-24","cited_by_count":12,"area":"safety"},{"id":"2311.00919","title":"MIST: Defending Against Membership Inference Attacks Through Membership-Invariant Subspace Training","created":"2023-11-02","cited_by_count":12,"area":"safety"},{"id":"2402.06289","title":"FedMIA: An Effective Membership Inference Attack Exploiting \"All for One\" Principle in Federated Learning","created":"2024-02-09","cited_by_count":12,"area":"safety"},{"id":"2104.08273","title":"Membership Inference Attacks on Knowledge Graphs","created":"2021-04-16","cited_by_count":11,"area":"safety"},{"id":"2310.00222","title":"Source Inference Attacks: Beyond Membership Inference Attacks in Federated Learning","created":"2023-09-30","cited_by_count":11,"area":"safety"},{"id":"2404.14296","title":"Does Your Neural Code Completion Model Use My Code? A Membership Inference Approach","created":"2024-04-22","cited_by_count":11,"area":"safety"},{"id":"2105.05381","title":"Accuracy-Privacy Trade-off in Deep Ensemble: A Membership Inference Perspective","created":"2021-05-12","cited_by_count":10,"area":"safety"},{"id":"2111.09076","title":"To Trust or Not To Trust Prediction Scores for Membership Inference Attacks","created":"2021-11-17","cited_by_count":10,"area":"safety"},{"id":"2207.12535","title":"Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning","created":"2022-07-25","cited_by_count":10,"area":"safety"},{"id":"2311.14056","title":"DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release","created":"2023-11-23","cited_by_count":10,"area":"safety"},{"id":"2103.03078","title":"Defending Medical Image Diagnostics against Privacy Attacks using Generative Methods","created":"2021-03-04","cited_by_count":9,"area":"safety"},{"id":"2404.02462","title":"A Unified Membership Inference Method for Visual Self-supervised Encoder via Part-aware Capability","created":"2024-04-03","cited_by_count":9,"area":"safety"},{"id":"2102.03314","title":"On Utility and Privacy in Synthetic Genomic Data","created":"2021-02-05","cited_by_count":8,"area":"safety"},{"id":"2211.15918","title":"Similarity Distribution based Membership Inference Attack on Person Re-identification","created":"2022-11-29","cited_by_count":8,"area":"safety"},{"id":"2310.11970","title":"Quantifying Privacy Risks of Prompts in Visual Prompt Learning","created":"2023-10-18","cited_by_count":8,"area":"safety"},{"id":"2312.07200","title":"Code Membership Inference for Detecting Unauthorized Data Use in Code Pre-trained Language Models","created":"2023-12-12","cited_by_count":8,"area":"safety"},{"id":"2212.01082","title":"Membership Inference Attacks Against Semantic Segmentation Models","created":"2022-12-02","cited_by_count":7,"area":"safety"},{"id":"2310.07219","title":"Improved Membership Inference Attacks Against Language Classification Models","created":"2023-10-11","cited_by_count":7,"area":"safety"},{"id":"2312.00051","title":"MIA-BAD: An Approach for Enhancing Membership Inference Attack and its Mitigation with Federated Learning","created":"2023-11-28","cited_by_count":7,"area":"safety"},{"id":"2406.19395","title":"Dataset Size Recovery from LoRA Weights","created":"2024-06-27","cited_by_count":7,"area":"safety"},{"id":"2407.01919","title":"A Method to Facilitate Membership Inference Attacks in Deep Learning Models","created":"2024-07-02","cited_by_count":7,"area":"safety"},{"id":"2606.14748","title":"Is My Vision-Language Data in Your AI? Membership Inference Test (MINT) Demo 2","created":"2026-06-05","cited_by_count":7,"area":"safety"},{"id":"2111.01363","title":"Knowledge Cross-Distillation for Membership Privacy","created":"2021-11-02","cited_by_count":6,"area":"safety"},{"id":"2111.05528","title":"Lightweight machine unlearning in neural network","created":"2021-11-10","cited_by_count":6,"area":"safety"},{"id":"2111.11869","title":"Machine unlearning via GAN","created":"2021-11-22","cited_by_count":6,"area":"safety"},{"id":"2203.00915","title":"MIAShield: Defending Membership Inference Attacks via Preemptive Exclusion of Members","created":"2022-03-02","cited_by_count":6,"area":"safety"},{"id":"2402.05453","title":"Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss","created":"2024-02-08","cited_by_count":6,"area":"safety"},{"id":"2405.07018","title":"Shadow-Free Membership Inference Attacks: Recommender Systems Are More Vulnerable Than You Thought","created":"2024-05-11","cited_by_count":6,"area":"safety"},{"id":"2501.11577","title":"Rethinking Membership Inference Attacks Against Transfer Learning","created":"2025-01-20","cited_by_count":6,"area":"safety"},{"id":"2507.21412","title":"Cascading and Proxy Membership Inference Attacks","created":"2025-07-29","cited_by_count":6,"area":"safety"}],"next_cursor":"100"}