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.
☆29Updated 4 years ago
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.☆68Updated 4 years ago
- Secure and utility-aware data collection with condensed local differential privacy☆17Updated 5 years ago
- Python implementation of differential privacy using Laplace mechanism☆11Updated 2 years ago
- Concentrated Differentially Private Gradient Descent with Adaptive per-iteration Privacy Budget☆49Updated 7 years ago
- Accompanying source code to my Bachelor's thesis at TUHH☆11Updated 7 years ago
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆49Updated 4 years ago
- Differential Privacy Preservation in Deep Learning under Model Attacks☆135Updated 4 years ago
- Amortized version of the differentially private SGD algorithm published in "Deep Learning with Differential Privacy" by Abadi et al. Enfo…☆41Updated last year
- Differential priavcy based federated learning framework by various neural networks and svm using PyTorch.☆33Updated 4 years ago
- PyTorch implementation of a number of mechanisms in local differential privacy☆17Updated 3 years ago
- Code for the CCS'22 paper "Federated Boosted Decision Trees with Differential Privacy"☆46Updated last year
- Implementation of Shuffled Model of Differential Privacy in Federated Learning." AISTATS, 2021.☆18Updated 2 years ago
- Differential priavcy based federated learning framework by various neural networks and svm using PyTorch.☆45Updated 2 years ago
- An implementation of Deep Learning with Differential Privacy☆25Updated 2 years ago
- Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness (IJCAI'19).☆13Updated 4 years ago
- Chain-PPFL: A Privacy-Preserving Federated Learning Framework based on Chained SMC☆35Updated 4 years ago
- Python package for simple implementations of state-of-the-art LDP frequency estimation algorithms. Contains code for our VLDB 2021 Paper.☆74Updated last year
- Code for the paper "Bayesian Differential Privacy for Machine Learning"☆22Updated 4 years ago
- FedAvg code with privacy protection function, the application of Paillier homomorphic encryption algorithm and differential privacy, diff…☆119Updated 8 months ago
- Implementation of calibration bounds for differential privacy in the shuffle model☆22Updated 4 years ago
- 本项目实现自《差分隐私下满足一致性的轨迹流量发布方法》,作者蔡剑平☆12Updated 5 years ago
- A foundational platform that primarily shares federated learning, differential privacy content☆22Updated 2 months ago
- FedShare: Secure Aggregation based on Additive Secret Sharing in Federated Learning☆20Updated 2 years ago
- Differentially Private Federated Learning on Heterogeneous Data☆64Updated 3 years ago
- Preserve data privacy with k-anonymity (samarati & mondrian), differential privacy, federated learning, paillier homomorphic encryption, …☆61Updated 3 years ago
- Implementation of local differential privacy mechanisms in Python language.☆29Updated 2 years ago
- This is an implementation for paper "A Hybrid Approach to Privacy Preserving Federated Learning" (https://arxiv.org/pdf/1812.03224.pdf)☆21Updated 4 years ago
- IEEE TIFS'20: VeriFL: Communication-Efficient and Fast Verifiable Aggregation for Federated Learning☆24Updated 2 years ago
- Privacy-preserving federated learning is distributed machine learning where multiple collaborators train a model through protected gradi…☆30Updated 3 years ago
- SAFEFL: MPC-friendly Framework for Private and Robust Federated Learning☆37Updated last year