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/jeffreysijuntan/cryptgpu
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU
https://github.com/jinyuan-jia/memguard
MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
https://github.com/jjy1994/MemGuard
MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
https://github.com/katekemu/mia
https://github.com/kingcong/models
Locally Differentially Private (Contextual) Bandits Learning
https://github.com/levyisthebest/echopulse_prelease
EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data Sharing
https://github.com/mindspore-ai/models/tree/master/research/rl/ldp_linucb
Locally Differentially Private (Contextual) Bandits Learning
https://github.com/ml-research/do-not-trust-prediction-scores-for-membership-inference-attacks
Membership Inference Attacks against Machine Learning Models
https://github.com/ml-research/to-trust-or-not-to-trust-prediction-scores-for-membership-inference-attacks
Membership Inference Attacks against Machine Learning Models
https://github.com/mukira/PySyft
https://github.com/nicomanzonelli/topic_model_attacks
https://github.com/nnnnai/ego4d_nlq_2022_1st_place_solution
https://github.com/obi-ml-public/ehr_deidentification
https://github.com/orientino/lira-pytorch
https://github.com/pyannote/pyannote-audio
https://github.com/richardobi/medigan
Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data
https://github.com/rupampatir/TrainingDataSynthesizer
Membership Inference Attacks against Machine Learning Models
https://github.com/spring-epfl/mia
Membership Inference Attacks against Machine Learning Models
https://github.com/trailofbits/privacyraven
https://github.com/tryIcatch/Memguard
MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
https://github.com/ubc-tea/DESA
https://github.com/unoriginalorigi/splitfss
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing
https://github.com/vanderschaarlab/hide-and-seek-submissions
https://github.com/vlgiitr/confidence-is-all-you-need
https://github.com/woodyx218/opacus_global_clipping
Tempered Sigmoid Activations for Deep Learning with Differential Privacy
https://github.com/wuzhenyusjtu/Privacy-AdversarialLearning
Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset
https://github.com/yechanp/Membership-Inference-Attacks-Against-Object-Detection-Models
Membership Inference Attacks Against Object Detection Models
https://github.com/yeliudev/R2-Tuning
https://github.com/yonsei-cysec/Membership_Inference_Attack
Membership Inference Attacks against Machine Learning Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.