DP-UTIL: A Comprehensive Utility Analysis of Differential Privacy in Machine Learning

This repository contains the source code accompanying our ACM CODASPY'22 paper DP-UTIL: A Comprehensive Utility Analysis of Differential Privacy in Machine Learning.

Notes:

• For Objective Perturbation, we used IBM Differential Privacy library

• For Gradient Perturbation, we used Tensorflow Privacy library.

• All required installations were done within Google colab.

• For LendingClub Loan and COVID-19 datasets, you have to add their path manually to your code.

LendingClub Loan:

• Dataset: https://www.kaggle.com/wordsforthewise/lending-club
• Logistic Regression: LR_Loan.ipynb
• DNN: DNN_Loan.ipynb

CIFAR-10:

• Logistic Regression: LR_Cifar10.ipynb
• DNN: DNN_Cifar10.ipynb

COVID-19:

• Dataset: https://www.kaggle.com/tanmoyx/covid19-patient-precondition-dataset
• Logistic Regression: LR_Covid19.ipynb
• DNN: DNN_Covid19.ipynb

Contact:

Ismat Jarin: ijarin@umich.edu