Network traces represent a critical piece of data for network security. Due to lack of expertise, companies are forced to outsource their network traces to third parties to perform analytics on the traces and provide security feedback and recommendations. However, these companies are reluctant to share their network traces, as they comprise sensitive information (e.g., IP addresses). Therefore, the network traces are anonymized to ensure the privacy of the data and preserve its utility. The latter guarantees that the essence of the data remains valid after anonymization, otherwise the analytics are useless. Existing solutions, such as CryptoPAN, preserves the data utility (by preserving the IP prefixes), but are vulnerable to semantic attacks.In this paper, we propose an anonymization solution, which is based on the Feistel, which is widely used in encryption systems, such as DES and Twofish. Our solution preserves both data privacy and its utility at the same time. We validate our solution using the Kddcup99 dataset and measure the data leakage (dual of privacy) provided by our solution. We evaluate the security of our solution using the avalanche property, which is widely used to measure the security of encryption systems. Moreover, the efficacy of our solution is evaluated against Injection attacks. Overall, the obtained results, avalanche property and resistance to Injection attacks, are appealing.