# Impact of Privacy Parameters on Deep Learning Models for Image Classification

Source: [Impact of Privacy Parameters on Deep Learning Models for Image Classification](<https://www.sota2.com/research/paper/68fa76251e209131cea6b7b7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:47:33.330Z

Authors: Basanta Chaulagain

Published: 2024-12-09T17:31:55.000Z

## Abstract

The project aims to develop differentially private deep learning models for image classification on CIFAR-10 datasets \\cite{cifar10} and analyze the impact of various privacy parameters on model accuracy. We have implemented five different deep learning models, namely ConvNet, ResNet18, EfficientNet, ViT, and DenseNet121 and three supervised classifiers namely K-Nearest Neighbors, Naive Bayes Classifier and Support Vector Machine. We evaluated the performance of these models under varying settings. Our best performing model to date is EfficientNet with test accuracy of $59.63\\%$ with the following parameters (Adam optimizer, batch size 256, epoch size 100, epsilon value 5.0, learning rate $1e-3$, clipping threshold 1.0, and noise multiplier 0.912).

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2412.06689>)

[PDF](<https://arxiv.org/pdf/2412.06689.pdf>)