{"total":23,"rows":[{"id":"2403.01218","title":"Inexact Unlearning Needs More Careful Evaluations to Avoid a False Sense of Privacy","created":"2024-03-02","cited_by_count":82,"area":"safety"},{"id":"2402.01822","title":"Building Guardrails for Large Language Models","created":"2024-02-02","cited_by_count":65,"area":"safety"},{"id":"2302.07225","title":"Bounding Training Data Reconstruction in DP-SGD","created":"2023-02-14","cited_by_count":46,"area":"safety"},{"id":"2409.19732","title":"Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement","created":"2024-09-29","cited_by_count":45,"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":"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":"2402.06674","title":"Impact of Dataset Properties on Membership Inference Vulnerability of Deep Transfer Learning","created":"2024-02-07","cited_by_count":7,"area":"safety"},{"id":"2411.05743","title":"Free Record-Level Privacy Risk Evaluation Through Artifact-Based Methods","created":"2024-11-08","cited_by_count":7,"area":"safety"},{"id":"2502.19770","title":"TAPE: Tailored Posterior Difference for Auditing of Machine Unlearning","created":"2025-02-27","cited_by_count":6,"area":"safety"},{"id":"2310.03838","title":"Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning","created":"2023-10-05","cited_by_count":5,"area":"safety"},{"id":"2510.13451","title":"Toward Efficient Inference Attacks: Shadow Model Sharing via Mixture-of-Experts","created":"2025-10-15","cited_by_count":4,"area":"safety"},{"id":"2412.21123","title":"ExpShield: Safeguarding Web Text from Unauthorized Crawling and LLM Exploitation","created":"2024-12-30","cited_by_count":3,"area":"safety"},{"id":"2601.18650","title":"FaLW: A Forgetting-aware Loss Reweighting for Long-tailed Unlearning","created":"2026-01-26","cited_by_count":3,"area":"safety"},{"id":"2506.22506","title":"SABRE-FL: Selective and Accurate Backdoor Rejection for Federated Prompt Learning","created":"2025-06-25","cited_by_count":2,"area":"safety"},{"id":"2507.08158","title":"A Unified Framework for Adversary-Aware Differential Privacy Bounds","created":"2025-07-10","cited_by_count":2,"area":"safety"},{"id":"2512.09953","title":"ZK-APEX: Zero-Knowledge Approximate Personalized Unlearning with Executable Proofs","created":"2025-12-09","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":"2306.04109","title":"Membership inference attack with relative decision boundary distance","created":"2023-06-07","cited_by_count":1,"area":"safety"},{"id":"2402.12861","title":"From Mean to Extreme: Formal Differential Privacy Bounds on the Success of Real-World Data Reconstruction Attacks","created":"2024-02-20","cited_by_count":1,"area":"safety"},{"id":"2507.04771","title":"Efficient Unlearning with Privacy Guarantees","created":"2025-07-07","cited_by_count":1,"area":"safety"},{"id":"2604.18697","title":"Beyond Indistinguishability: Measuring Extraction Risk in LLM APIs","created":"2026-04-20","cited_by_count":1,"area":"safety"},{"id":"2405.15423","title":"Lost in the Averages: A New Specific Setup to Evaluate Membership Inference Attacks Against Machine Learning Models","created":"2024-05-24","cited_by_count":0,"area":"safety"},{"id":"2506.01777","title":"DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems","created":"2025-06-02","cited_by_count":0,"area":"safety"}],"next_cursor":null}