Augmenting Multimedia Analysis
Iqra Tabassum, Sibghat Ullah Bazai · 2023
The rapid expansion of multimedia data has led to a significant increase in the demand for efficient and accurate analysis techniques. Deep learning has emerged as a powerful tool for analyzing multimedia data, due to its ability to learn hierarchical representations of data. However, the users’ privacy in crowdsensing and the use of sensitive data in deep learning raises significant privacy concerns. Sensitive data may be exposed when collecting the data, while training, or even after the trained model has been released. This chapter focuses on how to improve multimedia analysis by fusing differential privacy with deep learning. The approach utilizes the benefits of deep learning to extract high-level features from multimedia data while ensuring the privacy of individuals whose data is being used. We present a comprehensive framework for augmenting multimedia analysis that integrates differential privacy into the deep learning pipeline. Differential privacy (DP) has been shown to guarantee a high level of protection of privacy in data analysis. The approach preserves the user&s;s privacy by introducing random noise to the original data set, to the gradients during training, or to the learning parameters to prevent the model from overfitting on individual data points, and thus making the attacker unattainable to obtain sensitive data about a data set participant. The chapter aims to represent a comprehensive analysis of several multimedia analysis tasks, including images and videos. The developments are shown through suggested approaches, data sets, and benchmarks that demonstrate how the fusion of differential privacy and deep learning has the potential to lead to the creation of privacy-preserving multimedia analysis that can be used for a variety of purposes, including healthcare, finance, and security.