Skip to main content

Anonip is a tool to anonymize IP-addresses in log-files.

Project description

anonip

PyPI Python versions Build Status Coverage Black License

Digitale Gesellschaft https://www.digitale-gesellschaft.ch

Formerly Swiss Privacy Foundation https://www.privacyfoundation.ch/

Description

Anonip is a tool to anonymize IP addresses in log files.

It masks the last bits of IPv4 and IPv6 addresses. That way most of the relevant information is preserved, while the IP-address does not match a particular individuum anymore.

The log entries get directly piped from Apache to Anonip. The unmasked IP addresses are never written to any file.

With the help of cat, it's also possible to rewrite existing log files.

For usage with nginx see here: https://github.com/DigitaleGesellschaft/Anonip/issues/1

Features

  • Masks IP addresses in log files
  • Configurable amount of masked bits
  • The column containing the IP address can freely be chosen
  • Works for both access.log- and error.log files

Officially supported python versions

  • 2.7
  • 3.5
  • 3.6
  • 3.7

Dependencies

If you're using python version >=3.3, there are no external dependencies.

For python versions <3.3:

Invocation

usage: anonip.py [-h] [-4 INTEGER] [-6 INTEGER] [-i INTEGER] [-o FILE]
                 [-c INTEGER [INTEGER ...]] [-l STRING] [-r STRING] [-p] [-d]
                 [-v]

Anonip is a tool to anonymize IP-addresses in log files.

optional arguments:
  -h, --help            show this help message and exit
  -4 INTEGER, --ipv4mask INTEGER
                        truncate the last n bits (default: 12)
  -6 INTEGER, --ipv6mask INTEGER
                        truncate the last n bits (default: 84)
  -i INTEGER, --increment INTEGER
                        increment the IP address by n (default: 0)
  -o FILE, --output FILE
                        file to write to
  -c INTEGER [INTEGER ...], --column INTEGER [INTEGER ...]
                        assume IP address is in column n (1-based indexed;
                        default: 1)
  -l STRING, --delimiter STRING
                        log delimiter (default: " ")
  -r STRING, --replace STRING
                        replacement string in case address parsing fails
                        Example: 0.0.0.0)
  -p, --skip-private    do not mask addresses in private ranges. See IANA
                        Special-Purpose Address Registry.
  -d, --debug           print debug messages
  -v, --version         show program's version number and exit

Usage

In the Apache configuration (or the one of a vhost) the log output needs to get piped to anonip:

CustomLog "|/path/to/anonip.py [OPTIONS] --output /path/to/log" combined

That's it! All the IP addresses will be masked in the log now.

Alternative:

cat /path/to/orig_log | /path/to/anonip.py [OPTIONS] --output /path/to/log

As a python module

Read from stdin:

from anonip import Anonip


anonip = Anonip()
for line in anonip.run():
    print(line)

Manually feed lines:

from anonip import Anonip


data = ['1.1.1.1', '2.2.2.2', '3.3.3.3']
anonip = Anonip()

for line in data:
    print(anonip.process_line(line))

Python 2 or 3?

For compatibility reasons, anonip uses the shebang #! /usr/bin/env python. This will default to python2 on all Linux distributions except for Arch Linux. The performance of anonip can be improved by running it with python3. If python3 is available on your system, you should preferrably invoke anonip like this:

python3 -m anonip [OPTIONS]

or

python3 /path/to/anonip.py [OPTIONS]

Motivation

In most cases IP addresses are personal data as they refer to individuals (or at least their Internet connection). IP addresses - and the data associated with them - may therefore only be lawfully processed in accordance with the principles of the applicable data protection laws.

Storage of log files from web servers, for example, is only permitted within close time limits or with the voluntary consent of the persons concerned (as long as the information about the IP address is linkable to a person).

Anonip tries to avoid exactly that, but without losing the benefit of those log files.

With the masking of the last bits of IP addresses, we're still able to distinguish the log entries up to a certain degree. Compared to the entire removal of the IP-adresses, we're still able to make a rough geolocating as well as a reverse DNS lookup. But the otherwise distinct IP addresses do not match a particular individuum anymore.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

anonip-1.0.0.tar.gz (7.5 kB view hashes)

Uploaded Source

Built Distribution

anonip-1.0.0-py2.py3-none-any.whl (7.6 kB view hashes)

Uploaded Python 2 Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page