ChrisWaites / descent-to-deleteLinks
Erasing data from machine learning models! ✏️
☆12Updated 4 years ago
Alternatives and similar repositories for descent-to-delete
Users that are interested in descent-to-delete are comparing it to the libraries listed below
Sorting:
- Privacy Risks of Securing Machine Learning Models against Adversarial Examples☆44Updated 5 years ago
- ☆32Updated 11 months ago
- ☆18Updated 3 years ago
- Official implementation of "GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models" (CCS 2020)☆48Updated 3 years ago
- Membership Inference Attacks and Defenses in Neural Network Pruning☆28Updated 3 years ago
- Code for the paper: Label-Only Membership Inference Attacks☆66Updated 3 years ago
- verifying machine unlearning by backdooring☆20Updated 2 years ago
- ☆10Updated 4 years ago
- Code for the CSF 2018 paper "Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting"☆37Updated 6 years ago
- Code for the paper "Deep Partition Aggregation: Provable Defenses against General Poisoning Attacks"☆12Updated 2 years ago
- ☆24Updated 3 years ago
- Privacy attacks on Split Learning☆42Updated 3 years ago
- Official implementation of "When Machine Unlearning Jeopardizes Privacy" (ACM CCS 2021)☆48Updated 3 years ago
- ☆19Updated 2 years ago
- code release for "Unrolling SGD: Understanding Factors Influencing Machine Unlearning" published at EuroS&P'22☆22Updated 3 years ago
- ☆45Updated 5 years ago
- KNN Defense Against Clean Label Poisoning Attacks☆12Updated 3 years ago
- Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)☆54Updated 6 years ago
- [CCS 2021] "DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation" by Boxin Wang*, Fan Wu*, Yunhui Long…☆38Updated 3 years ago
- Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"☆57Updated 2 years ago
- Official implementation of "RelaxLoss: Defending Membership Inference Attacks without Losing Utility" (ICLR 2022)☆50Updated 2 years ago
- R-GAP: Recursive Gradient Attack on Privacy [Accepted at ICLR 2021]☆37Updated 2 years ago
- ☆80Updated 3 years ago
- A library for running membership inference attacks against ML models☆148Updated 2 years ago
- The code is for our NeurIPS 2019 paper: https://arxiv.org/abs/1910.04749☆34Updated 5 years ago
- The code for our Updates-Leak paper☆17Updated 5 years ago
- ☆19Updated 10 months ago
- Systematic Evaluation of Membership Inference Privacy Risks of Machine Learning Models☆127Updated last year
- Private Adaptive Optimization with Side Information (ICML '22)☆16Updated 3 years ago
- ☆66Updated 6 years ago