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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/AIPAG/SeqMIA

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SeqMIA: Sequential-Metric Based Membership Inference Attack

https://github.com/AIRI-Institute/GLiRA

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GLiRA: Black-Box Membership Inference Attack via Knowledge Distillation

https://github.com/AhmedSalem2/ML-Leaks

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ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

https://github.com/BehroozRazeghi/CLUB

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Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utility

https://github.com/BehroozRazeghi/Variational-Leakage

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Variational Leakage: The Role of Information Complexity in Privacy Leakage

https://github.com/Brophy-E/ECG_GAN_MBD

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Synthesis of Realistic ECG using Generative Adversarial Networks

https://github.com/DependableSystemsLab/code_poison_MIA

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A Method to Facilitate Membership Inference Attacks in Deep Learning Models

https://github.com/DingfanChen/GAN-Leaks

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GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models

https://github.com/Forsad/Stable-Polynomial-Train

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A Training Framework for Optimal and Stable Training of Polynomial Neural Networks

https://github.com/HongshengHu/membership-inference-machine-learning-literature

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Membership Inference Attacks on Machine Learning: A Survey

https://github.com/LaRiffle/AriaNN

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ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing

https://github.com/MinChen00/UnlearningLeaks

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When Machine Unlearning Jeopardizes Privacy

https://github.com/SAP-samples/security-research-dp-hierarchical-text

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On the privacy-utility trade-off in differentially private hierarchical text classification

https://github.com/SAP-samples/security-research-membership-inference-against-generative-networks

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Reconstruction and Membership Inference Attacks against Generative Models

https://github.com/SAP-samples/security-research-membership-inference-and-differential-privacy

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Assessing differentially private deep learning with Membership Inference

https://github.com/Shawn-Shan/fawkes

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Fawkes: Protecting Privacy against Unauthorized Deep Learning Models

https://github.com/TUM-AIMED/2.5DAttention

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

https://github.com/VITA-Group/PA-HMDB51

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Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset

https://github.com/aidotse/leakpro

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Technical Report for the Forgotten-by-Design Project: Targeted Obfuscation for Machine Learning

https://github.com/andron00e/jast

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Just a Simple Transformation is Enough for Data Protection in Vertical Federated Learning

https://github.com/ar-roy/dct-cryptonets

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DCT-CryptoNets: Scaling Private Inference in the Frequency Domain

https://github.com/ayu987/deep-regression-unlearning

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Deep Regression Unlearning

https://github.com/bridgesra/expm_nf_mimic3_results_code

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Are Normalizing Flows the Key to Unlocking the Exponential Mechanism?

https://github.com/cardwizard/membershipInferenceLottery

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Membership Inference Attacks on Lottery Ticket Networks

https://github.com/computationalprivacy/mosaic_memory

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The Mosaic Memory of Large Language Models

https://github.com/csong27/membership-inference

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Membership Inference Attacks against Machine Learning Models

https://github.com/drndr/bert_ent_attack

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Memorization of Named Entities in Fine-tuned BERT Models

https://github.com/duchenzhuang/flowencrypt

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Secure Data Sharing With Flow Model

https://github.com/ellisalicante/ma-fl-mia

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Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning

https://github.com/facebookresearch/label_dp_antipodes

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Antipodes of Label Differential Privacy: PATE and ALIBI

https://github.com/factral/privdl

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Privacy-Preserving Deep Learning Using Deformable Operators for Secure Task Learning

https://github.com/friedronaldo/primitives-ps

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Commonality in Natural Images Rescues GANs: Pretraining GANs with Generic and Privacy-free Synthetic Data

https://github.com/fseclab-osaka/mia-diffusion

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Membership Inference Attacks against Diffusion Models

https://github.com/g-pichler/dishonest_mia

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Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated Learning

https://github.com/giladcohen/sif_mi_attack

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Membership Inference Attack Using Self Influence Functions

https://github.com/hassanalikhatim/amia

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Membership Inference Attacks on DNNs using Adversarial Perturbations

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