Shubha23 / Laplace-and-Exponential-mechanisms-for-privacyLinks
Applying Laplace and exponential mechanisms to add random noise to data for differential privacy. Plotting MSE vs. epsilon.
☆31Updated last week
Alternatives and similar repositories for Laplace-and-Exponential-mechanisms-for-privacy
Users that are interested in Laplace-and-Exponential-mechanisms-for-privacy are comparing it to the libraries listed below
Sorting:
- Curated notebooks on how to train neural networks using differential privacy and federated learning.☆67Updated 4 years ago
- Differential Privacy Preservation in Deep Learning under Model Attacks☆135Updated 4 years ago
- FedAvg code with privacy protection function, the application of Paillier homomorphic encryption algorithm and differential privacy, diff…☆131Updated last year
- Differential private machine learning☆198Updated 3 years ago
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆49Updated 4 years ago
- Implementation of dp-based federated learning framework using PyTorch☆314Updated 2 years ago
- Python implementation of differential privacy using Laplace mechanism☆11Updated 3 years ago
- Python package for simple implementations of state-of-the-art LDP frequency estimation algorithms. Contains code for our VLDB 2021 Paper.☆78Updated last month
- Amortized version of the differentially private SGD algorithm published in "Deep Learning with Differential Privacy" by Abadi et al. Enfo…☆43Updated last year
- Useful tools for differential privacy☆219Updated 3 years ago
- Differential priavcy based federated learning framework by various neural networks and svm using PyTorch.☆46Updated 3 years ago
- A foundational platform that primarily shares federated learning, differential privacy content☆26Updated 9 months ago
- Code for the paper "Bayesian Differential Privacy for Machine Learning"☆23Updated 5 years ago
- Preserve data privacy with k-anonymity (samarati & mondrian), differential privacy, federated learning, paillier homomorphic encryption, …☆61Updated 3 years ago
- Code for the CCS'22 paper "Federated Boosted Decision Trees with Differential Privacy"☆53Updated 2 years ago
- Secure and utility-aware data collection with condensed local differential privacy☆17Updated 5 years ago
- Privacy-Preserving Deep Learning via Additively Homomorphic Encryption☆72Updated 5 years ago
- An implementation of Deep Learning with Differential Privacy☆26Updated 2 years ago
- An implementation of Secure Aggregation algorithm based on "Practical Secure Aggregation for Privacy-Preserving Machine Learning (Bonawit…☆93Updated 6 years ago
- Differential priavcy based federated learning framework by various neural networks and svm using PyTorch.☆35Updated 4 years ago
- simple Differential Privacy in PyTorch☆49Updated 5 years ago
- Dopamine: Differentially Private Federated Learning on Medical Data (AAAI - PPAI)☆76Updated 10 months ago
- Sample LDP implementation in Python☆128Updated 2 years ago
- Differentially Private Federated Learning on Heterogeneous Data☆70Updated 3 years ago
- PyTorch implementation of a number of mechanisms in local differential privacy☆17Updated 3 years ago
- Concentrated Differentially Private Gradient Descent with Adaptive per-iteration Privacy Budget☆49Updated 7 years ago
- An Efficient Learning Framework For Federated XGBoostUsing Secret Sharing And Distributed Optimization☆32Updated 9 months ago
- Pytorch implementation of paper Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (https://arxiv.org/abs/16…☆46Updated 4 years ago
- Code for Paper "Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption"☆34Updated 3 years ago
- A Simulator for Privacy Preserving Federated Learning☆96Updated 4 years ago