https://github.com/privacytrustlab/ml_privacy_meter
공식 코드ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning
Papers with Code 아카이브
Inference Attack
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/s3l-official/slmia-sr
공식 코드SLMIA-SR: Speaker-Level Membership Inference Attacks against Speaker Recognition Systems
https://github.com/salehzz/acmia
공식 코드Automatic Calibration for Membership Inference Attack on Large Language Models
https://github.com/shrezaei/MI-Attack
공식 코드https://github.com/shrezaei/MI-on-EL
공식 코드Accuracy-Privacy Trade-off in Deep Ensemble: A Membership Inference Perspective
https://github.com/soumyaxyz/Privacy-Preserving-Federated-Learning
공식 코드https://github.com/spin-umass/membershipwhiteboxattacks
공식 코드Efficient passive membership inference attack in federated learning
https://github.com/spring-epfl/disparate-vulnerability
공식 코드https://github.com/sprintml/privacy_attacks_against_iars
공식 코드https://github.com/synthetic-society/recon-synth
공식 코드A Linear Reconstruction Approach for Attribute Inference Attacks against Synthetic Data
https://github.com/t3humphries/non-iid-mia
공식 코드Investigating Membership Inference Attacks under Data Dependencies
https://github.com/tanjasper/parameters_or_privacy
공식 코드Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference
https://github.com/tleemann/gaussian_mip
공식 코드https://github.com/trucndt/ami
공식 코드Active Membership Inference Attack under Local Differential Privacy in Federated Learning
https://github.com/trustworthygnn/mia-gnn
공식 코드Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications
https://github.com/tsinghua-fib-lab/neurips2024_spv-mia
공식 코드https://github.com/uJ62JHD/Privacy-Preserving-Social-Network-Embedding
공식 코드Adversarial Privacy Preserving Graph Embedding against Inference Attack
https://github.com/ukplab/iclr2025-psa
공식 코드Differentially Private Steering for Large Language Model Alignment
https://github.com/um-dsp/DP-UTIL
공식 코드DP-UTIL: Comprehensive Utility Analysis of Differential Privacy in Machine Learning
https://github.com/um-dsp/PRICURE
공식 코드PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting
https://github.com/usc-sail/fed-ser-leakage
공식 코드Attribute Inference Attack of Speech Emotion Recognition in Federated Learning Settings
https://github.com/vasishtduddu/attinfexplanations
공식 코드https://github.com/vita-group/shake-to-leak
공식 코드Shake to Leak: Fine-tuning Diffusion Models Can Amplify the Generative Privacy Risk
https://github.com/wjfu99/MIA-LLMs
공식 코드https://github.com/wjfu99/mia-gen
공식 코드A Probabilistic Fluctuation based Membership Inference Attack for Diffusion Models
https://github.com/wjfu99/mia-tuner
공식 코드MIA-Tuner: Adapting Large Language Models as Pre-training Text Detector
https://github.com/xiehahha/aaai_ppai23_split_learning_leakage
공식 코드Label Inference Attack against Split Learning under Regression Setting
https://github.com/xinleihe/semi-leak
공식 코드Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning
https://github.com/xj231/featureinference-vfl
공식 코드Feature Inference Attack on Model Predictions in Vertical Federated Learning
https://github.com/yaliMa/Adversarial-Patch-3D
공식 코드Enhancing Real-World Adversarial Patches through 3D Modeling of Complex Target Scenes
https://github.com/yukehu/vlm_mia
공식 코드https://github.com/yyy01/pac
공식 코드https://github.com/zhenglisec/decision-based-mia
공식 코드https://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://github.com/zjunet/GPIA_NIPS
공식 코드Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach
https://github.com/AdrienBenamira/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.