High-speed prefix-preserving IP address anonymization for passive measurement systems
Passive network measurement and packet header trace collection are vital tools for network operation and research. To protect a user's privacy, it is necessary to anonymize header fields, particularly IP addresses. To preserve the correlation between IP addresses, prefix-preserving anonymization has been proposed. The limitations of this approach for a high-performance measurement system are the need for complex cryptographic computations and potentially large amounts of memory. We propose a new prefix-preserving anonymization algorithm, top-hash subtree-replicated anonymization.(TSA), that features three novel improvements: precomputation, replicated subtrees, and top hashing. TSA makes anonymization practical to be implemented on network processors or dedicated logic at Gigabit rates. The performance of TSA is compared with a conventional cryptography based prefix-preserving anonymization scheme which utilizes caching. TSA performs better as it requires no online cryptographic computation and a small number of memory lookups per packet. Our analytic comparison of the susceptibility to attacks between conventional anonymization and our approach shows that TSA performs better for small scale attacks and comparably for medium scale attacks. The processing cost for TSA is reduced by two orders of magnitude and the memory requirements are a few Megabytes. The ability to tune the memory requirements and security level makes TSA ideal for a broad range of network systems with different capabilities.
anonymization network measurement privacy