A Privacy Preserving Generative Adversarial Network for Image Data

Abstract

The extensive usage of online applications and social media has raised serious concerns from the public regarding the exposure of their personal information. So, there is a strong need for data anonymization to prevent privacy breaches and leakages. The era of attacks on databases and servers is an old trend. Now, most attacks are based on earning access to users’ private data. There are techniques like k-anonymity and l-diversity to protect Personally Identifiable Information (PII) from adversaries. However, these techniques still cannot provide security from homogeneity attacks, and their application is limited to structural data only. Till now, the frameworks are only available to anonymize the human face data in image format. In this paper, we proposed a new architecture for protecting privacy-related information in images of Indian vehicle number plates. We propose an architecture for anonymizing the vehicle number plates using Wasserstein’s Generative Adversarial Network (WGAN) by retaining the original data distribution even after anonymization. Our framework guarantees that it does not store any information while processing. Our main goal is to protect personal information from the image data. After anonymization, there is no similarity between the original and generated image. Our dataset includes a wide variety of license plates from all regions of India. Our work ensures that no human or a character recognition algorithm can recognize the characters from our anonymized images.