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De-identification
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De-identification is the task of detecting privacy-related entities in text, such as person names, emails and contact data.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Enhancing Clinical Models with Pseudo Data for De-identification
Apollo: A Posteriori Label-Only Membership Inference Attack Towards Machine Unlearning
When Better Features Mean Greater Risks: The Performance-Privacy Trade-Off in Contrastive Learning
Membership Inference Attacks on Sequence Models
An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks
RedactOR: An LLM-Powered Framework for Automatic Clinical Data De-Identification
Re-identification of De-identified Documents with Autoregressive Infilling
A Training Framework for Optimal and Stable Training of Polynomial Neural Networks
Securing Genomic Data Against Inference Attacks in Federated Learning Environments
AugMixCloak: A Defense against Membership Inference Attacks via Image Transformation
Automatic Calibration for Membership Inference Attack on Large Language Models
A new membership inference attack that spots memorization in generative and predictive models: Loss-Based with Reference Model algorithm (LBRM)
Large Language Model Empowered Privacy-Protected Framework for PHI Annotation in Clinical Notes
Palmprint De-Identification Using Diffusion Model for High-Quality and Diverse Synthesis
DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation
Natural Language Processing for Electronic Health Records in Scandinavian Languages: Norwegian, Swedish, and Danish
Graph-Level Label-Only Membership Inference Attack against Graph Neural Networks
Can Zero-Shot Commercial APIs Deliver Regulatory-Grade Clinical Text DeIdentification?
DP-GPL: Differentially Private Graph Prompt Learning
Efficient Membership Inference Attacks by Bayesian Neural Network
Split-n-Chain: Privacy-Preserving Multi-Node Split Learning with Blockchain-Based Auditability
Antibiotic Resistance Microbiology Dataset (ARMD): A De-identified Resource for Studying Antimicrobial Resistance Using Electronic Health Records
Towards Label-Only Membership Inference Attack against Pre-trained Large Language Models
Data-Constrained Synthesis of Training Data for De-Identification
Beyond De-Identification: A Structured Approach for Defining and Detecting Indirect Identifiers in Medical Texts
Membership Inference Attacks for Face Images Against Fine-Tuned Latent Diffusion Models
A hierarchical approach for assessing the vulnerability of tree-based classification models to membership inference attack
Zero-shot generation of synthetic neurosurgical data with large language models
The Devil is in the Prompts: De-Identification Traces Enhance Memorization Risks in Synthetic Chest X-Ray Generation
DocMIA: Document-Level Membership Inference Attacks against DocVQA Models
Privacy Attacks on Image AutoRegressive Models
Tool Unlearning for Tool-Augmented LLMs
Riddle Me This! Stealthy Membership Inference for Retrieval-Augmented Generation
Redefining Machine Unlearning: A Conformal Prediction-Motivated Approach
Differentially Private Steering for Large Language Model Alignment
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.