Repo containing the code of FPStalker's paper.

Create virtual environment and install dependencies

Run the command below to create a virtual environment.

virtualenv --python=/usr/bin/python3 myvenv

Then activate the virtual environment.

. myvenv/bin/activate

Finally, install the dependencies.

pip install -r requirements.txt

Database

Create a database that will contain the table that stores the fingerprints. Then, you have two solutions:

• Run the command below to generate a sql file tableFingerprints.sql with few fingerprints. It contains 15k fingerprints in this table that were randomly sampled from the first half of the raw dataset, i.e. with no filter. The reason we split the table in two files is to overcome the Github storage limit.

tar zxvf extension1.txt.tar.gz; tar zxvf extension2.txt.tar.gz; cat extension1.txt extension2.txt > tableFingerprints.sql

• Import extensionDataScheme.sql that contains only the scheme of the table to stores the fingerprints.

Change the connection to the database at the top of the main with your credentials.

Get ids of browser instances with countermeasures

python main.py getids

It generates a file called "consistent_extension_ids.csv" in data folder.

Launch evaluation process of a linking algorithm

python main.py auto myexpname nameoflinkingalgo 6

Where "myexpname" is the name of your experiment so that it can be used to prefix filenames, "nameoflinkingalgo" is either eckersley or rulebased, and 6 must be replaced by the minimum number of fingerprints a browser instance need to be part of the experiment.

For the Panopticlick/Eckersley linking algorithm

python main.py auto myexpname eckersley 6

For the rule-based linking algorithm

python main.py auto myexpname rulebased 6

For the hybrid linking algorithm

python main.py automl myexpname 6

In current state, it loads the random forest model contained in the data/my_ml_model. It was generated on the conditions specified in the article, i.e. To train a new model, one just needs to change the load parameter of the train_ml function (in main) to False. In order to optimize the lambda parameter, you just need to launch

python main.py lambda

Benchmark

For the hybrid algorithm:

python automlbench myfilesprefix 4

Where 4 has to be replaced by the number of cores on your machine.

For the rule-based algorithm:

python autorulesbench myfilesprefix 4