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

• 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.

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

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

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

Ismat Jarin: [email protected]