Privacy is of the utmost concern when it comes to releasing data to third parties. Data owners rely on anonymization approaches to safeguard the released datasets against re-identification attacks. However, even with strict anonymization in place, re-identification attacks are still a possibility and in many cases a reality. Prior art has focused on providing better anonymization algorithms with minimal loss of information and how to prevent data disclosure attacks. Our approach tries to tackle the issue of tracing re-identification attacks based on the concept of honeytokens, decoy or "bait" records with the goal to lure malicious users. While the concept of honeytokens has been widely used in the security domain, this is the first approach to apply the concept on the data privacy domain. Records with high re-identification risk, called AnonTokens, are inserted into anonymized datasets. This work demonstrates the feasibility, detectability and usability of AnonTokens and provides promising results for data owners who want to apply our approach to real use cases. We evaluated our concept with real large-scale population datasets. The results show that the introduction of decoy tokens is feasible without significant impact on the released dataset.