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Privacy Preserving Deep Learning
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The goal of privacy-preserving (deep) learning is to train a model while preserving privacy of the training dataset. Typically, it is understood that the trained model should be privacy-preserving (e.g., due to the training algorithm being differentially private).
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sisyphus-project/sisyphus-ppml
공식 코드https://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/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/tayebiarasteh/chestx
공식 코드Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels
https://github.com/tayebiarasteh/dp_cxr
공식 코드Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
https://github.com/tayebiarasteh/fldomain
공식 코드Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI Models
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/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/uvm-plaid/backpropagation-clipping
공식 코드Backpropagation Clipping for Deep Learning with Differential Privacy
https://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/xinleihe/semi-leak
공식 코드Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning
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://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/GeorgeTzannetos/ml-leaks-pytorch
https://github.com/Koukyosyumei/AIJack
Membership Inference Attacks against Machine Learning Models
https://github.com/Lab41/cyphercat
https://github.com/MichaelMurashov/ecg-testing
Synthesis of Realistic ECG using Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.