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Privacy Preserving

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/abureyanahmed/gnn-mcts-steiner

공식 코드

Nearly Optimal Steiner Trees using Graph Neural Network Assisted Monte Carlo Tree Search

https://github.com/abureyanahmed/gnn-msts-sparsification

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Graph Sparsifications using Neural Network Assisted Monte Carlo Tree Search

https://github.com/ace-kaist/pignet2

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PIGNet2: A Versatile Deep Learning-based Protein-Ligand Interaction Prediction Model for Binding Affinity Scoring and Virtual Screening

https://github.com/acecreamu/craquelure-graphs

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Craquelure as a Graph: Application of Image Processing and Graph Neural Networks to the Description of Fracture Patterns

https://github.com/acoursey3/freeway-anomaly-data

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FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalous Event Detection

https://github.com/adacheng/product_taxonomy_expansion

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Learning What You Need from What You Did: Product Taxonomy Expansion with User Behaviors Supervision

https://github.com/adibaorz/flism

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Federated Learning for Time-Series Healthcare Sensing with Incomplete Modalities

https://github.com/adis98/stv

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Share Secrets for Privacy: Confidential Forecasting with Vertical Federated Learning

https://github.com/adityamishraaaa/RiM

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RiM: Record, Improve and Maintain Physical Well-being using Federated Learning

https://github.com/adityasengar/ld-fpg

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Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings

https://github.com/adversarial-for-goodness/APF

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Adversarial Privacy-preserving Filter

https://github.com/adwise-fiu/level-site-ppdt

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Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees

https://github.com/aelgabli/fednew

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FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning

https://github.com/agave233/SIGN

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Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity

https://github.com/agiresearch/emojicrypt

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EmojiPrompt: Generative Prompt Obfuscation for Privacy-Preserving Communication with Cloud-based LLMs

https://github.com/agiresearch/promptcrypt

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EmojiPrompt: Generative Prompt Obfuscation for Privacy-Preserving Communication with Cloud-based LLMs

https://github.com/agoodge/lunar

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LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks

https://github.com/ahchang98/image-encryption-scheme-attacks

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Attacks on Image Encryption Schemes for Privacy-Preserving Deep Neural Networks

https://github.com/ahmedesamy/leap

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Leap: Inductive Link Prediction via Learnable TopologyAugmentation

https://github.com/ahmedradwan02/tinyeco2ai-nlp

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TinyML NLP Scheme for Semantic Wireless Sentiment Classification with Privacy Preservation

https://github.com/ahxt/graphconv_is_mixup

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On the Equivalence of Graph Convolution and Mixup

https://github.com/ai-secure/aug-pe

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Differentially Private Synthetic Data via Foundation Model APIs 2: Text

https://github.com/ai-secure/g-pate

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G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators

https://github.com/ai-secure/vfl-admm

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Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM

https://github.com/ai4reason/ATP_Proofs

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Learning Theorem Proving Components

https://github.com/ai4risk/antifraud

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Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation

https://github.com/aicps/sg-collision-prediction

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Spatio-Temporal Scene-Graph Embedding for Autonomous Vehicle Collision Prediction

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/aikunyi/fouriergnn

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FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective

https://github.com/ailab-unifi/da-graphtab

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Data augmentation on graphs for table type classification

https://github.com/airlabkhu/a2xp

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A2XP: Towards Private Domain Generalization

https://github.com/airtownapp/airtown-application

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Use of Air Quality Sensor Network Data for Real-time Pollution-Aware POI Suggestion

https://github.com/aitmlouk/fedn-client-fedqas-tf

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FedQAS: Privacy-aware machine reading comprehension with federated learning

https://github.com/aixwinnie/hetedp

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Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation

https://github.com/akensert/molgraph

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MolGraph: a Python package for the implementation of molecular graphs and graph neural networks with TensorFlow and Keras

https://github.com/akulen/privgen

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Privacy-Preserving Synthetic Educational Data Generation

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