In recent years, academic literature has analyzed many attacks on network trace anonymization techniques. These attacks usually correlate external information with anonymized data and successfully de-anonymize objects with distinctive signatures. However, analyses of these attacks still underestimate the real risk of publishing anonymized data, as the most powerful attack against anonymization is traffic injection. We demonstrate that performing live traffic injection attacks against anonymization on a backbone network is not difficult, and that potential countermeasures against these attacks, such as traffic aggregation, randomization or field generalization, are not particularly effective. We then discuss tradeoffs of the attacker and defender in the so-called injection attack space. An asymmetry in the attack space significantly increases the chance of a successful de-anonymization through lengthening the injected traffic pattern. This leads us to re-examine the role of network data anonymization. We recommend a unified approach to data sharing, which uses anonymization as a part of a technical, legal, and social approach to data protection in the research and operations communities.