A Lightweight Advertisement Ecosystem Simulation Platform for Security Analysis

Abstract

Based on the statistics, advertisements (ads) generate more than 80% of companies' revenues. However, the complexity of the ads ecosystem blurs the boundaries of responsibility between companies. It can not analyze privacy and security issues in such an ecosystem. For example, collecting user information without user consent for data analysis and displaying ads will cause privacy leakage. But we can not explain which types of the company need to take measures to protect privacy. In our paper, to clarify the responsibility of companies in the advertisement ecosystem, we divide them into six types of entities according to their needs and functions. Then, we design a lightweight simulation platform to illustrate the advertisement ecosystem and support security and privacy analysis. Finally, we take personal ads recommendations based on federated learning as an example to verify the feasibility for privacy and security analysis in this platform.