Papers with Code 아카이브
De-identification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/yukehu/vlm_mia
공식 코드https://github.com/yuweisunn/homogeneous-learning
공식 코드Homogeneous Learning: Self-Attention Decentralized Deep Learning
https://github.com/yyy01/pac
공식 코드https://github.com/zfscgy/Amber
공식 코드Towards Secure and Practical Machine Learning via Secret Sharing and Random Permutation
https://github.com/ziqi-zhang/teeslice-artifact
공식 코드No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device ML
https://gitlab.com/acit-lausanne/refacing-cgan
공식 코드https://github.com/AdrienBenamira/membership_inference_attack
Membership Inference Attacks against Machine Learning Models
https://github.com/Berenice2018/PySyft-Bc
https://github.com/BielStela/membership_inference
Understanding Membership Inferences on Well-Generalized Learning Models
https://github.com/Franck-Dernoncourt/NeuroNER
De-identification of Patient Notes with Recurrent Neural Networks
https://github.com/GeorgeTzannetos/ml-leaks-pytorch
https://github.com/Koukyosyumei/AIJack
Membership Inference Attacks against Machine Learning Models
https://github.com/Lab41/cyphercat
https://github.com/ManasRMohanty/DS5500-capstone
https://github.com/MichaelMurashov/ecg-testing
Synthesis of Realistic ECG using Generative Adversarial Networks
https://github.com/MikhailMurashov/ecgGAN
Synthesis of Realistic ECG using Generative Adversarial Networks
https://github.com/PrivPkt/PrivPkt
Membership Inference Attacks against Machine Learning Models
https://github.com/SJabin/Data_Model_Dependencies_MIA
https://github.com/SanaAwan5/transfer_learning_on_mnist_spdz
https://github.com/TAMU-VITA/Privacy-AdversarialLearning
Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset
https://github.com/TIO-IKIM/medical_image_deidentification
De-Identification of Medical Imaging Data: A Comprehensive Tool for Ensuring Patient Privacy
https://github.com/VITA-Group/Privacy-AdversarialLearning
Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset
https://github.com/Voice-Privacy-Challenge/Voice-Privacy-Challenge-2020
Speech Pseudonymisation Assessment Using Voice Similarity Matrices
https://github.com/Voice-Privacy-Challenge/Voice-Privacy-Challenge-2022
Speech Pseudonymisation Assessment Using Voice Similarity Matrices
https://github.com/YotamNitzan/ID-disentanglement
https://github.com/ali7naseh/RAG_MIA
Riddle Me This! Stealthy Membership Inference for Retrieval-Augmented Generation
https://github.com/arafin-lab/model_inversion_experiments
https://github.com/awslabs/sagemaker-privacy-for-nlp
Membership Inference Attacks against Machine Learning Models
https://github.com/danielroich/Face-Identity-Disentanglement-via-StyleGan2
https://github.com/danielroich/ID-disentanglement-Pytorch
https://github.com/ego4d/episodic-memory
https://github.com/ego4d/forecasting
https://github.com/ego4d/hands-and-objects
https://github.com/epilef68/cyphercat
https://github.com/frenchkrab/is2023-powerset-diarization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.