{"total":119,"rows":[{"id":"2103.07853","title":"Membership Inference Attacks on Machine Learning: A Survey","created":"2021-03-14","cited_by_count":321,"area":"safety"},{"id":"2106.04378","title":"Adaptive Machine Unlearning","created":"2021-06-08","cited_by_count":176,"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":"2109.08266","title":"Hard to Forget: Poisoning Attacks on Certified Machine Unlearning","created":"2021-09-17","cited_by_count":52,"area":"safety"},{"id":"2305.06360","title":"Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy","created":"2023-05-10","cited_by_count":51,"area":"safety"},{"id":"2204.09274","title":"Causality-based Neural Network Repair","created":"2022-04-20","cited_by_count":47,"area":"safety"},{"id":"2205.12709","title":"VeriFi: Towards Verifiable Federated Unlearning","created":"2022-05-25","cited_by_count":46,"area":"safety"},{"id":"2405.20272","title":"Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable","created":"2024-05-30","cited_by_count":25,"area":"safety"},{"id":"2203.12964","title":"Knowledge Removal in Sampling-based Bayesian Inference","created":"2022-03-24","cited_by_count":21,"area":"safety"},{"id":"2404.13588","title":"Machine Unlearning via Null Space Calibration","created":"2024-04-21","cited_by_count":21,"area":"safety"},{"id":"2211.11656","title":"SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization","created":"2022-11-21","cited_by_count":18,"area":"safety"},{"id":"2401.11018","title":"Communication Efficient and Provable Federated Unlearning","created":"2024-01-19","cited_by_count":18,"area":"safety"},{"id":"2404.00506","title":"Label-Agnostic Forgetting: A Supervision-Free Unlearning in Deep Models","created":"2024-03-31","cited_by_count":18,"area":"safety"},{"id":"2302.12448","title":"Subspace based Federated Unlearning","created":"2023-02-24","cited_by_count":17,"area":"safety"},{"id":"2401.15239","title":"MEA-Defender: A Robust Watermark against Model Extraction Attack","created":"2024-01-26","cited_by_count":17,"area":"safety"},{"id":"2412.20200","title":"Federated Unlearning with Gradient Descent and Conflict Mitigation","created":"2024-12-28","cited_by_count":17,"area":"safety"},{"id":"2205.13176","title":"On Collective Robustness of Bagging Against Data Poisoning","created":"2022-05-26","cited_by_count":16,"area":"safety"},{"id":"2305.00097","title":"NNSplitter: An Active Defense Solution for DNN Model via Automated Weight Obfuscation","created":"2023-04-28","cited_by_count":16,"area":"safety"},{"id":"2502.20709","title":"Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter","created":"2025-02-28","cited_by_count":16,"area":"safety"},{"id":"2105.13697","title":"AdvParams: An Active DNN Intellectual Property Protection Technique via Adversarial Perturbation Based Parameter Encryption","created":"2021-05-28","cited_by_count":13,"area":"safety"},{"id":"2203.02777","title":"Cosine Model Watermarking Against Ensemble Distillation","created":"2022-03-05","cited_by_count":13,"area":"safety"},{"id":"2108.01644","title":"The Devil is in the GAN: Backdoor Attacks and Defenses in Deep Generative Models","created":"2021-08-03","cited_by_count":12,"area":"safety"},{"id":"2306.01356","title":"FedCIP: Federated Client Intellectual Property Protection with Traitor Tracking","created":"2023-06-02","cited_by_count":12,"area":"safety"},{"id":"2509.26427","title":"Ascent Fails to Forget","created":"2025-09-30","cited_by_count":12,"area":"safety"},{"id":"2312.16823","title":"Layer Attack Unlearning: Fast and Accurate Machine Unlearning via Layer Level Attack and Knowledge Distillation","created":"2023-12-28","cited_by_count":11,"area":"safety"},{"id":"2405.17462","title":"Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity","created":"2024-05-23","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":"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":"2406.11544","title":"Do Parameters Reveal More than Loss for Membership Inference?","created":"2024-06-17","cited_by_count":10,"area":"safety"},{"id":"2102.09751","title":"PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting","created":"2021-02-19","cited_by_count":9,"area":"safety"},{"id":"2410.01276","title":"Deep Unlearn: Benchmarking Machine Unlearning for Image Classification","created":"2024-10-02","cited_by_count":9,"area":"safety"},{"id":"2205.06986","title":"Evaluating Membership Inference Through Adversarial Robustness","created":"2022-05-14","cited_by_count":8,"area":"safety"},{"id":"2304.00160","title":"Secure Federated Learning against Model Poisoning Attacks via Client Filtering","created":"2023-03-31","cited_by_count":8,"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":"2401.17504","title":"CaMU: Disentangling Causal Effects in Deep Model Unlearning","created":"2024-01-30","cited_by_count":7,"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":"2401.15917","title":"Blockchain-enabled Trustworthy Federated Unlearning","created":"2024-01-29","cited_by_count":6,"area":"safety"},{"id":"2401.15957","title":"Scalable Federated Unlearning via Isolated and Coded Sharding","created":"2024-01-29","cited_by_count":6,"area":"safety"},{"id":"2407.01306","title":"Unveiling the Unseen: Exploring Whitebox Membership Inference through the Lens of Explainability","created":"2024-07-01","cited_by_count":6,"area":"safety"},{"id":"2507.15836","title":"Optimizing Canaries for Privacy Auditing with Metagradient Descent","created":"2025-07-21","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"},{"id":"2104.09203","title":"Protecting the Intellectual Properties of Deep Neural Networks with an Additional Class and Steganographic Images","created":"2021-04-19","cited_by_count":5,"area":"safety"},{"id":"2306.02216","title":"Forgettable Federated Linear Learning with Certified Data Unlearning","created":"2023-06-03","cited_by_count":5,"area":"safety"},{"id":"2406.14507","title":"On Newton's Method to Unlearn Neural Networks","created":"2024-06-20","cited_by_count":5,"area":"safety"},{"id":"2407.15526","title":"Synthetic Image Learning: Preserving Performance and Preventing Membership Inference Attacks","created":"2024-07-22","cited_by_count":5,"area":"safety"},{"id":"2509.17530","title":"An Unlearning Framework for Continual Learning","created":"2025-09-22","cited_by_count":5,"area":"safety"},{"id":"2203.02080","title":"An Efficient Subpopulation-based Membership Inference Attack","created":"2022-03-04","cited_by_count":4,"area":"safety"},{"id":"2308.13269","title":"Heterogeneous Decentralized Machine Unlearning with Seed Model Distillation","created":"2023-08-25","cited_by_count":4,"area":"safety"},{"id":"2402.19054","title":"RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection","created":"2024-02-29","cited_by_count":4,"area":"safety"},{"id":"2405.15316","title":"Decaf: Data Distribution Decompose Attack against Federated Learning","created":"2024-05-24","cited_by_count":4,"area":"safety"},{"id":"2408.06063","title":"TruVRF: Towards Triple-Granularity Verification on Machine Unlearning","created":"2024-08-12","cited_by_count":4,"area":"safety"},{"id":"2412.08969","title":"Deep Learning Model Security: Threats and Defenses","created":"2024-12-12","cited_by_count":4,"area":"safety"},{"id":"2508.12730","title":"Unlearning Comparator: A Visual Analytics System for Comparative Evaluation of Machine Unlearning Methods","created":"2025-08-18","cited_by_count":4,"area":"safety"},{"id":"2212.02701","title":"On the Discredibility of Membership Inference Attacks","created":"2022-12-06","cited_by_count":3,"area":"safety"},{"id":"2306.01613","title":"Hyperparameter Learning under Data Poisoning: Analysis of the Influence of Regularization via Multiobjective Bilevel Optimization","created":"2023-06-02","cited_by_count":3,"area":"safety"},{"id":"2308.01421","title":"Regularization, early-stopping and dreaming: a Hopfield-like setup to address generalization and overfitting","created":"2023-08-01","cited_by_count":3,"area":"safety"},{"id":"2312.14260","title":"Elevating Defenses: Bridging Adversarial Training and Watermarking for Model Resilience","created":"2023-12-21","cited_by_count":3,"area":"safety"},{"id":"2402.10065","title":"Some Targets Are Harder to Identify than Others: Quantifying the Target-dependent Membership Leakage","created":"2024-02-15","cited_by_count":3,"area":"safety"},{"id":"2404.03180","title":"Goldfish: An Efficient Federated Unlearning Framework","created":"2024-04-04","cited_by_count":3,"area":"safety"},{"id":"2406.07885","title":"GENIU: A Restricted Data Access Unlearning for Imbalanced Data","created":"2024-06-12","cited_by_count":3,"area":"safety"},{"id":"2411.08443","title":"Machine Unlearning on Pre-trained Models by Residual Feature Alignment Using LoRA","created":"2024-11-13","cited_by_count":3,"area":"safety"},{"id":"2502.16737","title":"Keeping up with dynamic attackers: Certifying robustness to adaptive online data poisoning","created":"2025-02-23","cited_by_count":3,"area":"safety"},{"id":"2503.00062","title":"CRFU: Compressive Representation Forgetting Against Privacy Leakage on Machine Unlearning","created":"2025-02-27","cited_by_count":3,"area":"safety"},{"id":"2509.06796","title":"Imitative Membership Inference Attack","created":"2025-09-08","cited_by_count":3,"area":"safety"},{"id":"2511.13116","title":"Synthetic Forgetting without Access: A Few-shot Zero-glance Framework for Machine Unlearning","created":"2025-11-17","cited_by_count":3,"area":"safety"},{"id":"2407.00866","title":"Silver Linings in the Shadows: Harnessing Membership Inference for Machine Unlearning","created":"2024-07-01","cited_by_count":2,"area":"safety"},{"id":"2407.19119","title":"Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning","created":"2024-07-26","cited_by_count":2,"area":"safety"},{"id":"2408.01040","title":"Privacy-Preserving Split Learning with Vision Transformers using Patch-Wise Random and Noisy CutMix","created":"2024-08-02","cited_by_count":2,"area":"safety"},{"id":"2410.09947","title":"Efficient Federated Unlearning under Plausible Deniability","created":"2024-10-13","cited_by_count":2,"area":"safety"},{"id":"2410.22374","title":"Machine Unlearning using Forgetting Neural Networks","created":"2024-10-29","cited_by_count":2,"area":"safety"},{"id":"2503.23748","title":"THEMIS: Towards Practical Intellectual Property Protection for Post-Deployment On-Device Deep Learning Models","created":"2025-03-31","cited_by_count":2,"area":"safety"},{"id":"2505.14531","title":"SifterNet: A Generalized and Model-Agnostic Trigger Purification Approach","created":"2025-05-20","cited_by_count":2,"area":"safety"},{"id":"2512.19253","title":"Machine Unlearning in the Era of Quantum Machine Learning: An Empirical Study","created":"2025-12-22","cited_by_count":2,"area":"safety"},{"id":"2601.06436","title":"Certified Unlearning in Decentralized Federated Learning","created":"2026-01-10","cited_by_count":2,"area":"safety"},{"id":"2603.12142","title":"Understanding Disclosure Risk in Differential Privacy with Applications to Noise Calibration and Auditing (Extended Version)","created":"2026-03-12","cited_by_count":2,"area":"safety"},{"id":"2108.04206","title":"Classification Auto-Encoder based Detector against Diverse Data Poisoning Attacks","created":"2021-08-09","cited_by_count":1,"area":"safety"},{"id":"2206.00071","title":"Generative Models with Information-Theoretic Protection Against Membership Inference Attacks","created":"2022-05-31","cited_by_count":1,"area":"safety"},{"id":"2308.14322","title":"Machine Unlearning Methodology base on Stochastic Teacher Network","created":"2023-08-28","cited_by_count":1,"area":"safety"},{"id":"2311.05826","title":"Honest Score Client Selection Scheme: Preventing Federated Learning Label Flipping Attacks in Non-IID Scenarios","created":"2023-11-10","cited_by_count":1,"area":"safety"},{"id":"2408.02813","title":"Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense","created":"2024-08-05","cited_by_count":1,"area":"safety"},{"id":"2409.13004","title":"Data Poisoning and Leakage Analysis in Federated Learning","created":"2024-09-19","cited_by_count":1,"area":"safety"},{"id":"2411.03445","title":"Solving Trojan Detection Competitions with Linear Weight Classification","created":"2024-11-05","cited_by_count":1,"area":"safety"},{"id":"2412.12837","title":"Exposing the Vulnerability of Decentralized Learning to Membership Inference Attacks Through the Lens of Graph Mixing","created":"2024-12-17","cited_by_count":1,"area":"safety"},{"id":"2504.03173","title":"PPFPL: Cross-silo Privacy-preserving Federated Prototype Learning Against Data Poisoning Attacks","created":"2025-04-04","cited_by_count":1,"area":"safety"},{"id":"2505.16147","title":"Losing is for Cherishing: Data Valuation Based on Machine Unlearning and Shapley Value","created":"2025-05-22","cited_by_count":1,"area":"safety"},{"id":"2507.23291","title":"Evaluating the Dynamics of Membership Privacy in Deep Learning","created":"2025-07-31","cited_by_count":1,"area":"safety"},{"id":"2508.06244","title":"Membership Inference Attack with Partial Features","created":"2025-08-08","cited_by_count":1,"area":"safety"},{"id":"2508.13853","title":"FedUP: Efficient Pruning-based Federated Unlearning for Model Poisoning Attacks","created":"2025-08-19","cited_by_count":1,"area":"safety"},{"id":"2512.10348","title":"REMISVFU: Vertical Federated Unlearning via Representation Misdirection for Intermediate Output Feature","created":"2025-12-11","cited_by_count":1,"area":"safety"},{"id":"2601.06641","title":"Leveraging Soft Prompts for Privacy Attacks in Federated Prompt Tuning","created":"2026-01-10","cited_by_count":1,"area":"safety"},{"id":"2601.21695","title":"AtPatch: Debugging Transformers via Hot-Fixing Over-Attention","created":"2026-01-29","cited_by_count":1,"area":"safety"},{"id":"2602.16596","title":"Sequential Membership Inference Attacks","created":"2026-02-18","cited_by_count":1,"area":"safety"},{"id":"2603.14484","title":"Unlearning-based sliding window for continual learning under concept drift","created":"2026-03-15","cited_by_count":1,"area":"safety"},{"id":"2104.06744","title":"Defending Against Adversarial Denial-of-Service Data Poisoning Attacks","created":"2021-04-14","cited_by_count":0,"area":"safety"},{"id":"2303.17080","title":"Mole Recruitment: Poisoning of Image Classifiers via Selective Batch Sampling","created":"2023-03-30","cited_by_count":0,"area":"safety"},{"id":"2404.12852","title":"LSP Framework: A Compensatory Model for Defeating Trigger Reverse Engineering via Label Smoothing Poisoning","created":"2024-04-19","cited_by_count":0,"area":"safety"},{"id":"2405.06073","title":"Hard Work Does Not Always Pay Off: Poisoning Attacks on Neural Architecture Search","created":"2024-05-09","cited_by_count":0,"area":"safety"},{"id":"2410.06848","title":"Forgetting Through Transforming: Enabling Federated Unlearning via Class-Aware Representation Transformation","created":"2024-10-09","cited_by_count":0,"area":"safety"},{"id":"2501.06686","title":"Modeling Neural Networks with Privacy Using Neural Stochastic Differential Equations","created":"2025-01-12","cited_by_count":0,"area":"safety"}],"next_cursor":"100"}