Efficient differentially private kernel support vector classifier for multi-class classification

Python implementation for 'Efficient differentially private kernel support vector classifier for multi-class classification' published in Information Sciences Volume 619, January 2023, Pages 889-907. Please refer to https://www.sciencedirect.com/science/article/pii/S0020025522011951.

How to use

1. Run SVDD_DP.py file for default settings : You can change parameters in main function such as C (for SVDD), gamma (for RBF kernel), lr (for gradients when finding EPs), n_iter (for gradients when finding EPs), round_sep (for hypercube), averaged (for averaging n runs).
2. Specific Python implementataions for Multi-Basin Support-Based Clustering are in SVC.py.

Requirement

scipy sklearn numpy cvxopt

Citation

@article{park2023efficient,
  title={Efficient differentially private kernel support vector classifier for multi-class classification},
  author={Park, Jinseong and Choi, Yujin and Byun, Junyoung and Lee, Jaewook and Park, Saerom},
  journal={Information Sciences},
  volume={619},
  pages={889--907},
  year={2023},
  publisher={Elsevier}
}