An Identity Alignment Method based on Online Tracking

Companies track user data and sell it to advertisers. They claim to protect user privacy by anonymization, but our research shows that significant risks are still involved. Even with anonymous data, attackers can identify users on other websites from tracking records. We propose an identity alignment method of deanonymization attack, which analyzes tracker data to align identities. We explore the key factors affecting the effectiveness of identity alignment and analyze its impact on user privacy. We use crawling data to create tracker data close to ground-truth scenarios and propose an evaluation framework for online tracking based identity alignment.