Software releases

release of the cryptopANT library for IP address anonymization

cryptopANT v1.0 (stable) has been released (available at

cryptopANT is a C library for IP address anonymization using crypto-PAn algorithm, originally defined by Georgia Tech. The library supports anonymization and de-anonymization (provided you possess a secret key) of IPv4, IPv6, and MAC addresses. The software release includes sample utilities that anonymize IP addresses in text, but we expect most use of the library will be as part of other programs. The Crypto-PAn anonymization scheme was developed by Xu, Fan, Ammar, and Moon at Georgia Tech and described in“Prefix-Preserving IP Address Anonymization”, Computer Networks, Volume 46, Issue 2, 7 October 2004, Pages 253-272, Elsevier. Our library is independent (and not binary compatible) of theirs.

Despite this being the first release as a library, the code has been in use for more than 10 years in other tools.  It had been part of our other software packages, such as dag_scrubber for years.  By popular request, we’re finally releasing it as a separate package.

The library is packaged with an example binary (scramble_ips) that can be used to anonymize text ips.

See also the crypto-PAn page at Georgia Tech here.

Publications Technical Report

New Tech Report “Towards Geolocation of Millions of IP Addresses”

We just published a new technical report “Towards Geolocation of Millions of IP Addresses”, available at

From the abstract:

Previous measurement-based IP geolocation algorithms have focused on accuracy, studying a few targets with increasingly sophisticated algorithms taking measurements from tens of vantage points (VPs). In this paper, we study how to scale up existing measurement-based geolocation algorithms like Shortest Ping and CBG to cover the whole Internet. We show that with many vantage points, VP proximity to the target is the most important factor affecting accuracy. This observation suggests our new algorithm that selects the best few VPs for each target from many candidates. This approach addresses the main bottleneck to geolocation scalability: minimizing traffic into each target (and also out of each VP) while maintaining accuracy. Using this approach we have currently geolocated about 24% of the allocated, unicast, IPv4 address-space (about 55% of the addresses in the Internet that can be directly geolocated).