Papers with Code 아카이브

De-identification

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 2
평가표: 0
보존된 평가 기록: 0

De-identification is the task of detecting privacy-related entities in text, such as person names, emails and contact data.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/scotthlee/nrc

공식 코드

Natural Language Generation for Electronic Health Records

https://github.com/shams-sam/privacygans

공식 코드

Can we Generalize and Distribute Private Representation Learning?

https://github.com/shiqiyu/opengait

공식 코드

Pedestrian Attribute Editing for Gait Recognition and Anonymization

https://github.com/shrezaei/MI-on-EL

공식 코드

Accuracy-Privacy Trade-off in Deep Ensemble: A Membership Inference Perspective

https://github.com/sisaman/lpgnn

공식 코드

Locally Private Graph Neural Networks

https://github.com/sisyphus-project/sisyphus-ppml

공식 코드

Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning

https://github.com/soumyaxyz/Privacy-Preserving-Federated-Learning

공식 코드

MIA-BAD: An Approach for Enhancing Membership Inference Attack and its Mitigation with Federated Learning

https://github.com/spin-umass/membershipwhiteboxattacks

공식 코드

Efficient passive membership inference attack in federated learning

https://github.com/spring-epfl/disparate-vulnerability

공식 코드

Disparate Vulnerability to Membership Inference Attacks

https://github.com/sprintml/privacy_attacks_against_iars

공식 코드

Privacy Attacks on Image AutoRegressive Models

https://github.com/stanford-aimi/chexpert-plus

공식 코드

CheXpert Plus: Augmenting a Large Chest X-ray Dataset with Text Radiology Reports, Patient Demographics and Additional Image Formats

https://github.com/suamin/t2ner

공식 코드

Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts

https://github.com/suzhigangssz/avih

공식 코드

Hiding Visual Information via Obfuscating Adversarial Perturbations

https://github.com/t3humphries/non-iid-mia

공식 코드

Investigating Membership Inference Attacks under Data Dependencies

https://github.com/tanjasper/parameters_or_privacy

공식 코드

Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference

https://github.com/tayebiarasteh/chestx

공식 코드

Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels

https://github.com/tayebiarasteh/dp_cxr

공식 코드

Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging

https://github.com/tayebiarasteh/fldomain

공식 코드

Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI Models

https://github.com/tianyuan168326/mema-pytorch

공식 코드

Medical Manifestation-Aware De-Identification

https://github.com/tleemann/gaussian_mip

공식 코드

Gaussian Membership Inference Privacy

https://github.com/tmcarvalho/AUTOPRIV

공식 코드

Automated Privacy-Preserving Techniques via Meta-Learning

https://github.com/trucndt/ami

공식 코드

Active Membership Inference Attack under Local Differential Privacy in Federated Learning

https://github.com/trustworthygnn/mia-gnn

공식 코드

Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications

https://github.com/tsinghua-fib-lab/neurips2024_spv-mia

공식 코드

Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration

https://github.com/uds-lsv/privacy-preserving-text-transformer

공식 코드

Privacy Guarantees for De-identifying Text Transformations

https://github.com/ukplab/iclr2025-psa

공식 코드

Differentially Private Steering for Large Language Model Alignment

https://github.com/um-dsp/DP-UTIL

공식 코드

DP-UTIL: Comprehensive Utility Analysis of Differential Privacy in Machine Learning

https://github.com/um-dsp/PRICURE

공식 코드

PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting

https://github.com/uvm-plaid/backpropagation-clipping

공식 코드

Backpropagation Clipping for Deep Learning with Differential Privacy

https://github.com/viola-siemens/general-image-anonymization

공식 코드

SVIA: A Street View Image Anonymization Framework for Self-Driving Applications

https://github.com/vita-group/shake-to-leak

공식 코드

Shake to Leak: Fine-tuning Diffusion Models Can Amplify the Generative Privacy Risk

https://github.com/wjfu99/MIA-LLMs

공식 코드

Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration

https://github.com/wjfu99/mia-gen

공식 코드

A Probabilistic Fluctuation based Membership Inference Attack for Diffusion Models

https://github.com/wjfu99/mia-tuner

공식 코드

MIA-Tuner: Adapting Large Language Models as Pre-training Text Detector

https://github.com/xiaoyuxin1002/bias_in_deid

공식 코드

In the Name of Fairness: Assessing the Bias in Clinical Record De-identification

https://github.com/xinleihe/semi-leak

공식 코드

Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.