anusii / graph-dp
Implementations of differentially private release mechanisms for graph statistics
☆23Updated 3 years ago
Alternatives and similar repositories for graph-dp
Users that are interested in graph-dp are comparing it to the libraries listed below
Sorting:
- GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation (USENIX Security '23)☆47Updated last year
- Locally Private Graph Neural Networks (ACM CCS 2021)☆46Updated last year
- [IEEE S&P 22] "LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis" by Fan Wu, Yunhui Long, Ce Zhang, …☆23Updated 3 years ago
- ☆14Updated 2 months ago
- ☆20Updated last year
- ☆16Updated 3 years ago
- An implementation of "Data Synthesis via Differentially Private Markov Random Fields"☆14Updated last year
- Implementation of "PrivGraph: Differentially Private Graph Data Publication by Exploiting Community Information"☆13Updated 2 years ago
- Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness (IJCAI'19).☆13Updated 4 years ago
- Code for CCS '23 paper "Blink: Link Local Differential Privacy in Graph Neural Networks via Bayesian Estimation"☆11Updated last year
- ☆14Updated 4 years ago
- ☆28Updated 2 years ago
- Implementation of calibration bounds for differential privacy in the shuffle model☆22Updated 4 years ago
- ☆10Updated 3 years ago
- Analytic calibration for differential privacy with Gaussian perturbations☆47Updated 6 years ago
- ☆55Updated 2 years ago
- ☆32Updated 3 years ago
- Multiple Frequency Estimation Under Local Differential Privacy in Python☆47Updated last year
- Official implementation of "When Machine Unlearning Jeopardizes Privacy" (ACM CCS 2021)☆47Updated 2 years ago
- This repo implements several algorithms for learning with differential privacy.☆108Updated 2 years ago
- This repository contains the implementation of DPMLBench: Holistic Evaluation of Differentially Private Machine Learning☆10Updated last year
- This repository aims to provide links to works about privacy attacks and privacy preservation on graph data with Graph Neural Networks (G…☆22Updated last year
- PyTorch implementation of a number of mechanisms in local differential privacy☆17Updated 3 years ago
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆33Updated 4 years ago
- Code for Subgraph Federated Learning with Missing Neighbor Generation (NeurIPS 2021)☆75Updated 3 years ago
- Concentrated Differentially Private Gradient Descent with Adaptive per-iteration Privacy Budget☆49Updated 7 years ago
- Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"☆31Updated 3 years ago
- Implementation of paper "More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks"☆23Updated 2 years ago
- Hadamard Response: Communication efficient, sample optimal, linear time locally private learning of distributions☆14Updated 4 years ago
- Membership Inference, Attribute Inference and Model Inversion attacks implemented using PyTorch.☆61Updated 7 months ago