As location-based services are becoming more and more popular, new applications with location proximity functionality are expected to emerge. Furthermore, misconceptions about the actual privacy offered by various proximity models, has led services and major media into cultivating a false sense of security which has exposed users of these services to life-threatening risks. Evaluating the privacy guarantees of a large, diverse, and constantly evolving set of applications and services is not an easy task. As such we are releasing LBSProximityAuditor.
LBSProximityAuditor is an auditing framework for Location Based Services designed to facilitate researchers, developers, and privacy-sensitive individuals, in verifying the privacy offered by proximity applications.
This is a series of tests for the automatic evaluation of applications, regarding their query speed limiting, the verification of API parameters, and their effectiveness in protecting users against our precise user discovery attacks. It also provides a set of libraries that can be used to facilitate common operations on geographical data, both on natural and projected coordinates. Since we cannot predict what the specific requirements of each application might be, we expect auditors to implement the parts that are application-specific. In particular, our framework requires the auditor to implemented the following two API calls:
- auditor_get_distance(user_a, user_b), which returns the distance between two users of the service.
- auditor_set_location(user, lat, lon), which sets the location of a user at a specific set of coordinates.
Once the auditor implements the above calls, both the limit tests and the attacks work out of the box. In cases where the service does not use standard proximity oracles (i.e., exact distance, ring-based or disk-based) auditors can specify their own proximity oracle; subsequently, that can be passed as a parameter to LBSProximityAuditor for automatically evaluating the service. For thorough examples on how to implement your own auditing class for your service, please see the example_auditor.py file.
This software was designed by (alphabetically):
George Argyros [email protected]
Theofilos Petsios [email protected]
Jason Polakis [email protected]
Suphannee Sivakorn [email protected]
and developed by Theofilos Petsios at Columbia University, New York, NY, USA, in November 2014. You should receive a copy of the GPLv3 license with this document.
The required packages for running LBSProximityAuditor are included in requirements.txt. You may install the respective packages according to your OS / Python version. For instance, for a Debian running Python 2.7 one can run
sudo apt-get update && sudo apt-get upgrade
sudo apt-get install python-dev python-shapely python-lxml
pip install pykml pyproj requests
Alternatively, if you have virtualenv configured, you may install packages via
pip install -r requirements.txt
When running tests, a database named 'testing.db' is created and holds all information about the experiments that have been run, the users in each experiment, their queries, the limits of the service etc. In addition, a kml file is produced for each step of the auditing under files/kml. Finally, a json file with all the steps of the RUDP/DUDP attack is also produced for each attack that has been succesfully carried out.
Users can define their own exception handlers as well as their own proximity oracles to be used with the rest of this API. See the API functions for more info on each class.
Once the auditor class is properly set up for your service, you simply invoke it via
python your_auditor_class.py
An example of how to invoke the framework is given in example_auditor.py. In this example, we have implemented a class Tester which inherits from the Auditor class and implements auditor_get_distance and auditor_set_location functions.
!!! The example_auditor.py file as provided is not a working example !!!
This framework is intended for security research purposes only. Our goal is to assist security researchers and service developers in improving the privacy offered to users. Under no circumstances do we condone the use of our framework as an offensive tool.
Reverse engineering the protocols and API calls of various services may violate the Terms and Conditions of those services.